
import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns
from datetime import datetime
from datetime import date
from dateutil.relativedelta import relativedelta
from datetime import timedelta
import plotly.graph_objects as go
from plotly.subplots import make_subplots

#pip install pykalman
from pykalman import KalmanFilter

#pip install quantstats
import quantstats as qs


Futures_Data = pd.read_excel('Futures_Data.xlsx')
Futures_Data.index = pd.to_datetime(Futures_Data.index)

Futures_orig = Futures_Data.set_index('Date')

beginning_headers = ['USD Future',
 'EUR Future (USD)',
 'JPY Future (USD)',
 'GBP Future (USD)',
 'GBP Future (EUR)',
 'CAD Future (USD)',
 'CHF Future (USD)',
 'S&P 500',
 'Nasdaq 100',
 'DJIA',
 'Russell 2000',
 'S&P 400',
 'S&P Canada 60',
 'FTSE 100',
 'DAX',
 'SMI',
 'CAC - 40',
 'IBEX 35',
 'MIB 30',
 'OMX',
 'Euro Stoxx 50',
 'SPI 200',
 'Nikkei 225',
 'Hang Seng Index',
 'US MSCI EAFE',
 'US 2-year',
 'US 5-year',
 'US 10-year',
 'US Ultra 10-year',
 'US 30-year',
 'Short Gilt (2 year)',
 'Medium Gilt (5 year)',
 'Long Gilt (10 year)',
 'Euro-Schatz-Futures (2 year)',
 'Euro-Bobl-Future (5 year)',
 'Euro-Bund-Future (10 year)',
 'Euro-Buxl-Future (30 year)',
 'Brent Crude Oil',
 'WTI Crude Oil',
 'Heating Oil',
 'RBOB Gasoline',
 'Natural Gas',
 'Gold',
 'Silver',
 'Platinum',
 'Palladium',
 'Copper',
 'Nickel',
 'Zinc',
 'Lean hogs',
 'Live cattle',
 'Feeder cattle',
 'Wheat',
 'Corn',
 'Oat',
 'Soybeans',
 'Soybean oil',
 'Frozen orange juice',
 'Cocoa',
 'Coffee',
 'Sugar No.11',
 'Cotton No.2'
]

# Set the new headers
Futures_orig.columns = beginning_headers

Futures_orig


Futures_orig.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 6521 entries, 1999-01-01 to 2023-12-29
Data columns (total 62 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   USD Future                    6446 non-null   float64
 1   EUR Future (USD)              6342 non-null   float64
 2   JPY Future (USD)              6343 non-null   float64
 3   GBP Future (USD)              6343 non-null   float64
 4   GBP Future (EUR)              6050 non-null   float64
 5   CAD Future (USD)              6342 non-null   float64
 6   CHF Future (USD)              6345 non-null   float64
 7   S&P 500                       6340 non-null   float64
 8   Nasdaq 100                    6226 non-null   float64
 9   DJIA                          5484 non-null   float64
 10  Russell 2000                  1633 non-null   float64
 11  S&P 400                       5547 non-null   float64
 12  S&P Canada 60                 6107 non-null   float64
 13  FTSE 100                      6406 non-null   float64
 14  DAX                           6368 non-null   float64
 15  SMI                           6354 non-null   float64
 16  CAC - 40                      6415 non-null   float64
 17  IBEX 35                       6359 non-null   float64
 18  MIB 30                        5027 non-null   float64
 19  OMX                           4749 non-null   float64
 20  Euro Stoxx 50                 6368 non-null   float64
 21  SPI 200                       6074 non-null   float64
 22  Nikkei 225                    6129 non-null   float64
 23  Hang Seng Index               6191 non-null   float64
 24  US MSCI EAFE                  3690 non-null   float64
 25  US 2-year                     6297 non-null   float64
 26  US 5-year                     6294 non-null   float64
 27  US 10-year                    6289 non-null   float64
 28  US Ultra 10-year              2009 non-null   float64
 29  US 30-year                    3524 non-null   float64
 30  Short Gilt (2 year)           3633 non-null   float64
 31  Medium Gilt (5 year)          3633 non-null   float64
 32  Long Gilt (10 year)           6410 non-null   float64
 33  Euro-Schatz-Futures (2 year)  6372 non-null   float64
 34  Euro-Bobl-Future (5 year)     6370 non-null   float64
 35  Euro-Bund-Future (10 year)    6371 non-null   float64
 36  Euro-Buxl-Future (30 year)    6345 non-null   float64
 37  Brent Crude Oil               6414 non-null   float64
 38  WTI Crude Oil                 6242 non-null   float64
 39  Heating Oil                   6241 non-null   float64
 40  RBOB Gasoline                 4563 non-null   float64
 41  Natural Gas                   6280 non-null   float64
 42  Gold                          6282 non-null   float64
 43  Silver                        6281 non-null   float64
 44  Platinum                      6280 non-null   float64
 45  Palladium                     6279 non-null   float64
 46  Copper                        6281 non-null   float64
 47  Nickel                        6290 non-null   float64
 48  Zinc                          6308 non-null   float64
 49  Lean hogs                     6301 non-null   float64
 50  Live cattle                   6300 non-null   float64
 51  Feeder cattle                 6298 non-null   float64
 52  Wheat                         6298 non-null   float64
 53  Corn                          6297 non-null   float64
 54  Oat                           6298 non-null   float64
 55  Soybeans                      6296 non-null   float64
 56  Soybean oil                   6299 non-null   float64
 57  Frozen orange juice           6315 non-null   float64
 58  Cocoa                         6271 non-null   float64
 59  Coffee                        6270 non-null   float64
 60  Sugar No.11                   6270 non-null   float64
 61  Cotton No.2                   6309 non-null   float64
dtypes: float64(62)
memory usage: 3.1 MB


Futures_orig.describe()


dfs = {}

for column in Futures_orig.columns:
    # Create a new DataFrame for each column using the same index
    dfs[column] = pd.DataFrame(Futures_orig[column])

beginning_dates_dict = {}
    
for header in beginning_headers:
    if header in dfs:
        print(f"{header}:")
        
        beginning_date = dfs[header].apply(lambda col: col.first_valid_index()).iloc[0]
        beginning_dates_dict[header] = beginning_date
        print(f'Beginning date for: {beginning_date}')
        
        print(dfs[header].isnull().sum())
        num_rows_before = len(dfs[header].index)
        print(f'Number of rows for {header} before dropping NAs: {num_rows_before}')
        
        dfs[header].dropna(inplace=True)
        
        print(dfs[header].isnull().sum())
        num_rows_after = len(dfs[header].index)
        print(f'Number of rows for {header} after dropping NAs: {num_rows_after}')
        print("-" * 40)  # Just for visual separation between outputs
    else:
        print(f"The key '{header}' does not exist in the dfs dictionary.")
           
# Convert the beginning dates dictionary to a DataFrame
beginning_dates_df = pd.DataFrame(list(beginning_dates_dict.items()), columns=['Header', 'Beginning Date'])

# Convert string dates to datetime objects if they are not already (skip if not necessary)
for header, date_str in beginning_dates_dict.items():
    beginning_dates_dict[header] = pd.to_datetime(date_str)

# Filter the dictionary to include only dates greater than 1999
beginning_dates_after_1999_list = [header for header, date in beginning_dates_dict.items() if date.year > 1999]

USD Future:
Beginning date for: 1999-01-04 00:00:00
USD Future    75
dtype: int64
Number of rows for USD Future before dropping NAs: 6521
USD Future    0
dtype: int64
Number of rows for USD Future after dropping NAs: 6446
----------------------------------------
EUR Future (USD):
Beginning date for: 1999-01-04 00:00:00
EUR Future (USD)    179
dtype: int64
Number of rows for EUR Future (USD) before dropping NAs: 6521
EUR Future (USD)    0
dtype: int64
Number of rows for EUR Future (USD) after dropping NAs: 6342
----------------------------------------
JPY Future (USD):
Beginning date for: 1999-01-04 00:00:00
JPY Future (USD)    178
dtype: int64
Number of rows for JPY Future (USD) before dropping NAs: 6521
JPY Future (USD)    0
dtype: int64
Number of rows for JPY Future (USD) after dropping NAs: 6343
----------------------------------------
GBP Future (USD):
Beginning date for: 1999-01-04 00:00:00
GBP Future (USD)    178
dtype: int64
Number of rows for GBP Future (USD) before dropping NAs: 6521
GBP Future (USD)    0
dtype: int64
Number of rows for GBP Future (USD) after dropping NAs: 6343
----------------------------------------
GBP Future (EUR):
Beginning date for: 1999-01-12 00:00:00
GBP Future (EUR)    471
dtype: int64
Number of rows for GBP Future (EUR) before dropping NAs: 6521
GBP Future (EUR)    0
dtype: int64
Number of rows for GBP Future (EUR) after dropping NAs: 6050
----------------------------------------
CAD Future (USD):
Beginning date for: 1999-01-04 00:00:00
CAD Future (USD)    179
dtype: int64
Number of rows for CAD Future (USD) before dropping NAs: 6521
CAD Future (USD)    0
dtype: int64
Number of rows for CAD Future (USD) after dropping NAs: 6342
----------------------------------------
CHF Future (USD):
Beginning date for: 1999-01-04 00:00:00
CHF Future (USD)    176
dtype: int64
Number of rows for CHF Future (USD) before dropping NAs: 6521
CHF Future (USD)    0
dtype: int64
Number of rows for CHF Future (USD) after dropping NAs: 6345
----------------------------------------
S&P 500:
Beginning date for: 1999-01-04 00:00:00
S&P 500    181
dtype: int64
Number of rows for S&P 500 before dropping NAs: 6521
S&P 500    0
dtype: int64
Number of rows for S&P 500 after dropping NAs: 6340
----------------------------------------
Nasdaq 100:
Beginning date for: 1999-06-22 00:00:00
Nasdaq 100    295
dtype: int64
Number of rows for Nasdaq 100 before dropping NAs: 6521
Nasdaq 100    0
dtype: int64
Number of rows for Nasdaq 100 after dropping NAs: 6226
----------------------------------------
DJIA:
Beginning date for: 2002-04-05 00:00:00
DJIA    1037
dtype: int64
Number of rows for DJIA before dropping NAs: 6521
DJIA    0
dtype: int64
Number of rows for DJIA after dropping NAs: 5484
----------------------------------------
Russell 2000:
Beginning date for: 2017-07-11 00:00:00
Russell 2000    4888
dtype: int64
Number of rows for Russell 2000 before dropping NAs: 6521
Russell 2000    0
dtype: int64
Number of rows for Russell 2000 after dropping NAs: 1633
----------------------------------------
S&P 400:
Beginning date for: 2002-01-29 00:00:00
S&P 400    974
dtype: int64
Number of rows for S&P 400 before dropping NAs: 6521
S&P 400    0
dtype: int64
Number of rows for S&P 400 after dropping NAs: 5547
----------------------------------------
S&P Canada 60:
Beginning date for: 1999-09-08 00:00:00
S&P Canada 60    414
dtype: int64
Number of rows for S&P Canada 60 before dropping NAs: 6521
S&P Canada 60    0
dtype: int64
Number of rows for S&P Canada 60 after dropping NAs: 6107
----------------------------------------
FTSE 100:
Beginning date for: 1999-01-04 00:00:00
FTSE 100    115
dtype: int64
Number of rows for FTSE 100 before dropping NAs: 6521
FTSE 100    0
dtype: int64
Number of rows for FTSE 100 after dropping NAs: 6406
----------------------------------------
DAX:
Beginning date for: 1999-01-04 00:00:00
DAX    153
dtype: int64
Number of rows for DAX before dropping NAs: 6521
DAX    0
dtype: int64
Number of rows for DAX after dropping NAs: 6368
----------------------------------------
SMI:
Beginning date for: 1999-01-04 00:00:00
SMI    167
dtype: int64
Number of rows for SMI before dropping NAs: 6521
SMI    0
dtype: int64
Number of rows for SMI after dropping NAs: 6354
----------------------------------------
CAC - 40:
Beginning date for: 1999-01-05 00:00:00
CAC - 40    106
dtype: int64
Number of rows for CAC - 40 before dropping NAs: 6521
CAC - 40    0
dtype: int64
Number of rows for CAC - 40 after dropping NAs: 6415
----------------------------------------
IBEX 35:
Beginning date for: 1999-01-04 00:00:00
IBEX 35    162
dtype: int64
Number of rows for IBEX 35 before dropping NAs: 6521
IBEX 35    0
dtype: int64
Number of rows for IBEX 35 after dropping NAs: 6359
----------------------------------------
MIB 30:
Beginning date for: 2004-03-23 00:00:00
MIB 30    1494
dtype: int64
Number of rows for MIB 30 before dropping NAs: 6521
MIB 30    0
dtype: int64
Number of rows for MIB 30 after dropping NAs: 5027
----------------------------------------
OMX:
Beginning date for: 2005-02-15 00:00:00
OMX    1772
dtype: int64
Number of rows for OMX before dropping NAs: 6521
OMX    0
dtype: int64
Number of rows for OMX after dropping NAs: 4749
----------------------------------------
Euro Stoxx 50:
Beginning date for: 1999-01-04 00:00:00
Euro Stoxx 50    153
dtype: int64
Number of rows for Euro Stoxx 50 before dropping NAs: 6521
Euro Stoxx 50    0
dtype: int64
Number of rows for Euro Stoxx 50 after dropping NAs: 6368
----------------------------------------
SPI 200:
Beginning date for: 2000-05-03 00:00:00
SPI 200    447
dtype: int64
Number of rows for SPI 200 before dropping NAs: 6521
SPI 200    0
dtype: int64
Number of rows for SPI 200 after dropping NAs: 6074
----------------------------------------
Nikkei 225:
Beginning date for: 1999-01-04 00:00:00
Nikkei 225    392
dtype: int64
Number of rows for Nikkei 225 before dropping NAs: 6521
Nikkei 225    0
dtype: int64
Number of rows for Nikkei 225 after dropping NAs: 6129
----------------------------------------
Hang Seng Index:
Beginning date for: 1999-01-04 00:00:00
Hang Seng Index    330
dtype: int64
Number of rows for Hang Seng Index before dropping NAs: 6521
Hang Seng Index    0
dtype: int64
Number of rows for Hang Seng Index after dropping NAs: 6191
----------------------------------------
US MSCI EAFE:
Beginning date for: 2009-09-09 00:00:00
US MSCI EAFE    2831
dtype: int64
Number of rows for US MSCI EAFE before dropping NAs: 6521
US MSCI EAFE    0
dtype: int64
Number of rows for US MSCI EAFE after dropping NAs: 3690
----------------------------------------
US 2-year:
Beginning date for: 1999-01-04 00:00:00
US 2-year    224
dtype: int64
Number of rows for US 2-year before dropping NAs: 6521
US 2-year    0
dtype: int64
Number of rows for US 2-year after dropping NAs: 6297
----------------------------------------
US 5-year:
Beginning date for: 1999-01-04 00:00:00
US 5-year    227
dtype: int64
Number of rows for US 5-year before dropping NAs: 6521
US 5-year    0
dtype: int64
Number of rows for US 5-year after dropping NAs: 6294
----------------------------------------
US 10-year:
Beginning date for: 1999-01-04 00:00:00
US 10-year    232
dtype: int64
Number of rows for US 10-year before dropping NAs: 6521
US 10-year    0
dtype: int64
Number of rows for US 10-year after dropping NAs: 6289
----------------------------------------
US Ultra 10-year:
Beginning date for: 2016-01-12 00:00:00
US Ultra 10-year    4512
dtype: int64
Number of rows for US Ultra 10-year before dropping NAs: 6521
US Ultra 10-year    0
dtype: int64
Number of rows for US Ultra 10-year after dropping NAs: 2009
----------------------------------------
US 30-year:
Beginning date for: 2010-01-12 00:00:00
US 30-year    2997
dtype: int64
Number of rows for US 30-year before dropping NAs: 6521
US 30-year    0
dtype: int64
Number of rows for US 30-year after dropping NAs: 3524
----------------------------------------
Short Gilt (2 year):
Beginning date for: 2009-11-24 00:00:00
Short Gilt (2 year)    2888
dtype: int64
Number of rows for Short Gilt (2 year) before dropping NAs: 6521
Short Gilt (2 year)    0
dtype: int64
Number of rows for Short Gilt (2 year) after dropping NAs: 3633
----------------------------------------
Medium Gilt (5 year):
Beginning date for: 2009-11-24 00:00:00
Medium Gilt (5 year)    2888
dtype: int64
Number of rows for Medium Gilt (5 year) before dropping NAs: 6521
Medium Gilt (5 year)    0
dtype: int64
Number of rows for Medium Gilt (5 year) after dropping NAs: 3633
----------------------------------------
Long Gilt (10 year):
Beginning date for: 1999-01-04 00:00:00
Long Gilt (10 year)    111
dtype: int64
Number of rows for Long Gilt (10 year) before dropping NAs: 6521
Long Gilt (10 year)    0
dtype: int64
Number of rows for Long Gilt (10 year) after dropping NAs: 6410
----------------------------------------
Euro-Schatz-Futures (2 year):
Beginning date for: 1999-01-04 00:00:00
Euro-Schatz-Futures (2 year)    149
dtype: int64
Number of rows for Euro-Schatz-Futures (2 year) before dropping NAs: 6521
Euro-Schatz-Futures (2 year)    0
dtype: int64
Number of rows for Euro-Schatz-Futures (2 year) after dropping NAs: 6372
----------------------------------------
Euro-Bobl-Future (5 year):
Beginning date for: 1999-01-04 00:00:00
Euro-Bobl-Future (5 year)    151
dtype: int64
Number of rows for Euro-Bobl-Future (5 year) before dropping NAs: 6521
Euro-Bobl-Future (5 year)    0
dtype: int64
Number of rows for Euro-Bobl-Future (5 year) after dropping NAs: 6370
----------------------------------------
Euro-Bund-Future (10 year):
Beginning date for: 1999-01-04 00:00:00
Euro-Bund-Future (10 year)    150
dtype: int64
Number of rows for Euro-Bund-Future (10 year) before dropping NAs: 6521
Euro-Bund-Future (10 year)    0
dtype: int64
Number of rows for Euro-Bund-Future (10 year) after dropping NAs: 6371
----------------------------------------
Euro-Buxl-Future (30 year):
Beginning date for: 1999-01-04 00:00:00
Euro-Buxl-Future (30 year)    176
dtype: int64
Number of rows for Euro-Buxl-Future (30 year) before dropping NAs: 6521
Euro-Buxl-Future (30 year)    0
dtype: int64
Number of rows for Euro-Buxl-Future (30 year) after dropping NAs: 6345
----------------------------------------
Brent Crude Oil:
Beginning date for: 1999-01-04 00:00:00
Brent Crude Oil    107
dtype: int64
Number of rows for Brent Crude Oil before dropping NAs: 6521
Brent Crude Oil    0
dtype: int64
Number of rows for Brent Crude Oil after dropping NAs: 6414
----------------------------------------
WTI Crude Oil:
Beginning date for: 1999-01-04 00:00:00
WTI Crude Oil    279
dtype: int64
Number of rows for WTI Crude Oil before dropping NAs: 6521
WTI Crude Oil    0
dtype: int64
Number of rows for WTI Crude Oil after dropping NAs: 6242
----------------------------------------
Heating Oil:
Beginning date for: 1999-01-04 00:00:00
Heating Oil    280
dtype: int64
Number of rows for Heating Oil before dropping NAs: 6521
Heating Oil    0
dtype: int64
Number of rows for Heating Oil after dropping NAs: 6241
----------------------------------------
RBOB Gasoline:
Beginning date for: 2005-10-04 00:00:00
RBOB Gasoline    1958
dtype: int64
Number of rows for RBOB Gasoline before dropping NAs: 6521
RBOB Gasoline    0
dtype: int64
Number of rows for RBOB Gasoline after dropping NAs: 4563
----------------------------------------
Natural Gas:
Beginning date for: 1999-01-04 00:00:00
Natural Gas    241
dtype: int64
Number of rows for Natural Gas before dropping NAs: 6521
Natural Gas    0
dtype: int64
Number of rows for Natural Gas after dropping NAs: 6280
----------------------------------------
Gold:
Beginning date for: 1999-01-04 00:00:00
Gold    239
dtype: int64
Number of rows for Gold before dropping NAs: 6521
Gold    0
dtype: int64
Number of rows for Gold after dropping NAs: 6282
----------------------------------------
Silver:
Beginning date for: 1999-01-04 00:00:00
Silver    240
dtype: int64
Number of rows for Silver before dropping NAs: 6521
Silver    0
dtype: int64
Number of rows for Silver after dropping NAs: 6281
----------------------------------------
Platinum:
Beginning date for: 1999-01-04 00:00:00
Platinum    241
dtype: int64
Number of rows for Platinum before dropping NAs: 6521
Platinum    0
dtype: int64
Number of rows for Platinum after dropping NAs: 6280
----------------------------------------
Palladium:
Beginning date for: 1999-01-04 00:00:00
Palladium    242
dtype: int64
Number of rows for Palladium before dropping NAs: 6521
Palladium    0
dtype: int64
Number of rows for Palladium after dropping NAs: 6279
----------------------------------------
Copper:
Beginning date for: 1999-01-04 00:00:00
Copper    240
dtype: int64
Number of rows for Copper before dropping NAs: 6521
Copper    0
dtype: int64
Number of rows for Copper after dropping NAs: 6281
----------------------------------------
Nickel:
Beginning date for: 1999-01-04 00:00:00
Nickel    231
dtype: int64
Number of rows for Nickel before dropping NAs: 6521
Nickel    0
dtype: int64
Number of rows for Nickel after dropping NAs: 6290
----------------------------------------
Zinc:
Beginning date for: 1999-01-04 00:00:00
Zinc    213
dtype: int64
Number of rows for Zinc before dropping NAs: 6521
Zinc    0
dtype: int64
Number of rows for Zinc after dropping NAs: 6308
----------------------------------------
Lean hogs:
Beginning date for: 1999-01-04 00:00:00
Lean hogs    220
dtype: int64
Number of rows for Lean hogs before dropping NAs: 6521
Lean hogs    0
dtype: int64
Number of rows for Lean hogs after dropping NAs: 6301
----------------------------------------
Live cattle:
Beginning date for: 1999-01-04 00:00:00
Live cattle    221
dtype: int64
Number of rows for Live cattle before dropping NAs: 6521
Live cattle    0
dtype: int64
Number of rows for Live cattle after dropping NAs: 6300
----------------------------------------
Feeder cattle:
Beginning date for: 1999-01-04 00:00:00
Feeder cattle    223
dtype: int64
Number of rows for Feeder cattle before dropping NAs: 6521
Feeder cattle    0
dtype: int64
Number of rows for Feeder cattle after dropping NAs: 6298
----------------------------------------
Wheat:
Beginning date for: 1999-01-04 00:00:00
Wheat    223
dtype: int64
Number of rows for Wheat before dropping NAs: 6521
Wheat    0
dtype: int64
Number of rows for Wheat after dropping NAs: 6298
----------------------------------------
Corn:
Beginning date for: 1999-01-04 00:00:00
Corn    224
dtype: int64
Number of rows for Corn before dropping NAs: 6521
Corn    0
dtype: int64
Number of rows for Corn after dropping NAs: 6297
----------------------------------------
Oat:
Beginning date for: 1999-01-04 00:00:00
Oat    223
dtype: int64
Number of rows for Oat before dropping NAs: 6521
Oat    0
dtype: int64
Number of rows for Oat after dropping NAs: 6298
----------------------------------------
Soybeans:
Beginning date for: 1999-01-04 00:00:00
Soybeans    225
dtype: int64
Number of rows for Soybeans before dropping NAs: 6521
Soybeans    0
dtype: int64
Number of rows for Soybeans after dropping NAs: 6296
----------------------------------------
Soybean oil:
Beginning date for: 1999-01-04 00:00:00
Soybean oil    222
dtype: int64
Number of rows for Soybean oil before dropping NAs: 6521
Soybean oil    0
dtype: int64
Number of rows for Soybean oil after dropping NAs: 6299
----------------------------------------
Frozen orange juice:
Beginning date for: 1999-01-04 00:00:00
Frozen orange juice    206
dtype: int64
Number of rows for Frozen orange juice before dropping NAs: 6521
Frozen orange juice    0
dtype: int64
Number of rows for Frozen orange juice after dropping NAs: 6315
----------------------------------------
Cocoa:
Beginning date for: 1999-01-04 00:00:00
Cocoa    250
dtype: int64
Number of rows for Cocoa before dropping NAs: 6521
Cocoa    0
dtype: int64
Number of rows for Cocoa after dropping NAs: 6271
----------------------------------------
Coffee:
Beginning date for: 1999-01-04 00:00:00
Coffee    251
dtype: int64
Number of rows for Coffee before dropping NAs: 6521
Coffee    0
dtype: int64
Number of rows for Coffee after dropping NAs: 6270
----------------------------------------
Sugar No.11:
Beginning date for: 1999-01-04 00:00:00
Sugar No.11    251
dtype: int64
Number of rows for Sugar No.11 before dropping NAs: 6521
Sugar No.11    0
dtype: int64
Number of rows for Sugar No.11 after dropping NAs: 6270
----------------------------------------
Cotton No.2:
Beginning date for: 1999-01-04 00:00:00
Cotton No.2    212
dtype: int64
Number of rows for Cotton No.2 before dropping NAs: 6521
Cotton No.2    0
dtype: int64
Number of rows for Cotton No.2 after dropping NAs: 6309
----------------------------------------


beginning_dates_after_1999_list

['DJIA',
 'Russell 2000',
 'S&P 400',
 'MIB 30',
 'OMX',
 'SPI 200',
 'US MSCI EAFE',
 'US Ultra 10-year',
 'US 30-year',
 'Short Gilt (2 year)',
 'Medium Gilt (5 year)',
 'RBOB Gasoline']


new_headers = [header for header in beginning_headers if header not in beginning_dates_after_1999_list]
Futures = Futures_orig.drop(columns=beginning_dates_after_1999_list)

for header in beginning_dates_after_1999_list:
    if header in dfs:
        del dfs[header]


corr_matrix = Futures.corr()
plt.figure(figsize=(50, 50))
sns.heatmap(corr_matrix, annot=True, fmt=".2f")
plt.show()


# Define the slices for each DataFrame as a dictionary
slices = {
    'FX': (0, 7),
    'Equity': (7, 18),
    'Rates': (18, 26),
    'Commodity': (26, 50),
    'Energy': (26, 30),
    'Metals': (30, 37),
    'Agriculture': (37, 50),
}

for name, (start_col, end_col) in slices.items():
    temp_df = Futures.iloc[:, start_col:end_col]
    
    corr_matrix = temp_df.corr()
    
    #Plot
    plt.figure(figsize=(10, 8))
    sns.heatmap(corr_matrix, annot=True, fmt=".2f")
    plt.title(f'{name} Correlation Matrix')
    plt.show()


## Used to create the Statistics --> WTI not changed

# Initialize a DataFrame to store the annualized metrics
annualized_metrics_df_statistics = pd.DataFrame(columns=['Header', 'Annualized Mean Return', 'Annualized Volatility'])

for header in new_headers:
    if header in dfs:
        # Calculate daily returns
        dfs[header]['Daily Returns'] = dfs[header][header].pct_change()
        dfs[header]['Log Returns'] = np.log(dfs[header][header] / dfs[header][header].shift(1))
    
        # Calculate annualized mean return and volatility
        daily_mean_return = dfs[header]['Daily Returns'].mean()
        annualized_mean_return_statistics = daily_mean_return * 252
        daily_volatility = dfs[header]['Daily Returns'].std()
        annualized_volatility_statistics = daily_volatility * np.sqrt(252)
        
        # Append the metrics to the annualized_metrics_df DataFrame
        annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
                                                               'Annualized Mean Return': annualized_mean_return_statistics,
                                                               'Annualized Volatility': annualized_volatility_statistics},
                                                              ignore_index=True)
    else:
        print(f"The key '{header}' does not exist in the dfs dictionary.")
        
annualized_metrics_df_statistics.set_index('Header', inplace = True)
annualized_metrics_to_use = annualized_metrics_df_statistics.T

<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
C:\Users\Raphael\anaconda3\lib\site-packages\pandas\core\arraylike.py:402: RuntimeWarning: invalid value encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,
<ipython-input-10-1115b33ec1c3>:19: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  annualized_metrics_df_statistics = annualized_metrics_df_statistics.append({'Header': header,


## Used for Calculation of the Strategies --> Zero Value of WTI set to "zero"

for header in new_headers:
    if header in dfs:
        dfs[header][header] = np.where(dfs[header][header] <= 0, 0.01, dfs[header][header])

        # Calculate daily returns using adjusted prices
        dfs[header]['Daily Returns'] = dfs[header][header].pct_change()
        dfs[header]['Log Returns'] = np.log(dfs[header][header] / dfs[header][header].shift(1))

    else:
        print(f"The key '{header}' does not exist in the dfs dictionary.")


Summary_statistics = Futures.describe()

beginning_dates_orig = beginning_dates_df.set_index('Header')
beginning_dates_transposed = beginning_dates_orig.T
beginning_dates_clean = beginning_dates_transposed.drop(columns=beginning_dates_after_1999_list)

Summary_statistics_appended = pd.concat([Summary_statistics, beginning_dates_clean, annualized_metrics_to_use])
Summary_statistics_appended_dropped = Summary_statistics_appended.drop(['mean', 'std', '25%', '50%', '75%'])

Summary_statistics_for_Thesis = Summary_statistics_appended_dropped.T
Summary_statistics_for_Thesis.to_excel('Summary_statistics.xlsx')

<ipython-input-12-3cc21df50464>:11: UserWarning: Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).
  Summary_statistics_for_Thesis.to_excel('Summary_statistics.xlsx')


import plotly.graph_objects as go

features = new_headers

for feature in features:
    trace = go.Scatter(x=Futures.index, y=Futures[feature], mode='lines', name='Closing Price')
    data = [trace]
    layout = go.Layout(title=f'{feature} over Time', xaxis=dict(title='Days'), yaxis=dict(title=f'{feature} Price'))
    fig = go.Figure(data=data, layout=layout)
    fig.show()


# Line Zinc
trace_Zinc = go.Scatter(
    x=Futures.index, 
    y=Futures['Zinc'], 
    mode='lines', 
    name='Zinc'
)

# Combine all traces
data = [trace_Zinc]

# Define layout
layout = go.Layout(
    title={
        'text': 'Zinc price over time',
        'y': 0.9,
        'x': 0.5,
        'xanchor': 'center',
        'yanchor': 'top',
        'font': dict(size=18, color='black', family='Arial'),
    },
    plot_bgcolor='white',
    paper_bgcolor='white',
    xaxis=dict(
        showgrid=True,
        gridcolor='lightgrey',
        tickangle=-45  # Rotate the x-axis labels by 45 degrees
    ),
    yaxis=dict(
        title='Zinc price',
        showgrid=True,
        gridcolor='lightgrey'
    )
)

# Create figure and add data and layout
fig = go.Figure(data=data, layout=layout)

# Show the figure
fig.show()


# Window sizes for the moving averages
short_SMA = 1
long_SMA = 200

# Iterate over the headers and apply the moving averages
for header in new_headers:
    if header in dfs:
        # Calculate the short-window moving average and add it as a new column
        dfs[header][f'SMA_{short_SMA}'] = dfs[header][header].rolling(window=short_SMA).mean()

        # Calculate the long-window moving average and add it as a new column
        dfs[header][f'SMA_{long_SMA}'] = dfs[header][header].rolling(window=long_SMA).mean()
    else:
        print(f"DataFrame for {header} not found in dfs.")


# Iterate over the headers and apply the moving averages
for header in new_headers:
    if header in dfs:
        # Generate signals
        dfs[header]['Signal_SMA'] = 0
        # Buy signal when
        dfs[header]['Signal_SMA'] = np.where(dfs[header][f'SMA_{short_SMA}'] > dfs[header][f'SMA_{long_SMA}'], 1, dfs[header]['Signal_SMA'])
        # Sell signal
        dfs[header]['Signal_SMA'] = np.where(dfs[header][f'SMA_{short_SMA}'] < dfs[header][f'SMA_{long_SMA}'], -1, dfs[header]['Signal_SMA'])

        # Generate trading orders
        # Conditions
        Buy = dfs[header]['Signal_SMA'].diff() > 0
        Sell = dfs[header]['Signal_SMA'].diff() < 0

        # Choices based on conditions
        choices = [1, -1]

        # Use np.select to apply conditions and choices
        dfs[header]['Trades_SMA'] = np.select([Buy, Sell], choices, default=0)
        
    else:
        print(f"DataFrame for {header} not found in dfs.")


# Window sizes
short_SMA_with_Band = 1
long_SMA_with_Band = 200
Band_width = 0.01

# Iterate over the headers and apply the moving averages
for header in new_headers:
    if header in dfs:
        # Calculate the short-window moving average and add it as a new column
        dfs[header][f'SMA_{short_SMA_with_Band}_with_Band'] = dfs[header][header].rolling(window=short_SMA).mean()

        # Calculate the long-window moving average and add it as a new column
        dfs[header][f'SMA_{long_SMA_with_Band}_with_Band'] = dfs[header][header].rolling(window=long_SMA).mean()
        
        # Calculate 1% price band
        dfs[header]['SMA_with_Band_UpperBand'] = dfs[header][f'SMA_{long_SMA_with_Band}_with_Band'] * (1 + Band_width)
        dfs[header]['SMA_with_Band_LowerBand'] = dfs[header][f'SMA_{long_SMA_with_Band}_with_Band'] * (1 - Band_width)      
        
    else:
        print(f"DataFrame for {header} not found in dfs.")


for header in new_headers:
    if header in dfs:
        dfs[header]['Signal_SMA_with_Band'] = 0
        
        # Generate initial buy and sell signals based on SMA and Band conditions
        dfs[header]['Signal_SMA_with_Band'] = np.where(
            (dfs[header][f'SMA_{short_SMA_with_Band}_with_Band'] > dfs[header][f'SMA_{long_SMA_with_Band}_with_Band']) & 
            (dfs[header][f'SMA_{short_SMA_with_Band}_with_Band'] > dfs[header]['SMA_with_Band_UpperBand']), 1, 
            dfs[header]['Signal_SMA_with_Band']
        )  
        
        dfs[header]['Signal_SMA_with_Band'] = np.where(
            (dfs[header][f'SMA_{short_SMA_with_Band}_with_Band'] < dfs[header][f'SMA_{long_SMA_with_Band}_with_Band']) & 
            (dfs[header][f'SMA_{short_SMA_with_Band}_with_Band'] < dfs[header]['SMA_with_Band_LowerBand']), -1, 
            dfs[header]['Signal_SMA_with_Band']
        )

        # To ensure that we only consider "real changes" in the signal,
        # we need to track the last non-zero signal.
        # We can do this by creating a temporary Series that holds the last non-zero value seen at each step.
        last_non_zero_signal_with_Band = dfs[header]['Signal_SMA_with_Band'].replace(0, np.nan).ffill().fillna(0)

        # Now, generate 'Position_SMA_with_Band' based on real changes
        real_change_buy_with_Band = (dfs[header]['Signal_SMA_with_Band'] == 1) & (last_non_zero_signal_with_Band.shift(1) == -1)
        real_change_sell_with_Band = (dfs[header]['Signal_SMA_with_Band'] == -1) & (last_non_zero_signal_with_Band.shift(1) == 1)

        # Use np.select to apply the real change conditions
        dfs[header]['Trades_SMA_with_Band'] = np.select([real_change_buy_with_Band, real_change_sell_with_Band], [1, -1], default=0)
    else:
        print(f"DataFrame for {header} not found in dfs.")


# Window size
TSMOM_window = 252 # 12-month momentum
TSMOM_window_month = int(TSMOM_window / 21)

# Iterate over the headers and apply the moving averages
for header in new_headers:
    if header in dfs:
        # Calculate the short-window moving average and add it as a new column
        dfs[header][f'TSMOM_{TSMOM_window_month}'] = dfs[header]['Log Returns'].rolling(window=TSMOM_window).sum().shift(1)
        
    else:
        print(f"DataFrame for {header} not found in dfs.")


# Iterate over the headers and apply the moving averages
for header in new_headers:
    # Make sure the DataFrame exists in your dictionary
    if header in dfs:
        # Generate signals
        dfs[header][f'Signal_TSMOM_{TSMOM_window_month}'] = 0
        # Buy signal when
        dfs[header][f'Signal_TSMOM_{TSMOM_window_month}'] = np.where(dfs[header][f'TSMOM_{TSMOM_window_month}'] > 0, 1, dfs[header][f'Signal_TSMOM_{TSMOM_window_month}'])
        # Sell signal
        dfs[header][f'Signal_TSMOM_{TSMOM_window_month}'] = np.where(dfs[header][f'TSMOM_{TSMOM_window_month}'] < 0, -1, dfs[header][f'Signal_TSMOM_{TSMOM_window_month}'])

        # Generate trading orders
        # Conditions
        Buy = dfs[header][f'Signal_TSMOM_{TSMOM_window_month}'].diff() > 0
        Sell = dfs[header][f'Signal_TSMOM_{TSMOM_window_month}'].diff() < 0

        # Choices based on conditions
        choices = [1, -1]

        # Use np.select to apply conditions and choices
        dfs[header][f'Trades_TSMOM_{TSMOM_window_month}'] = np.select([Buy, Sell], choices, default=0)
        
    else:
        print(f"DataFrame for {header} not found in dfs.")


# Window size
RAMOM_window = 25 # 25-day momentum
RAMOM_window_month = RAMOM_window
RAMOM_return_window = 12 # according to Dudler et al they used 12-day returns
RAMOM_volatlity_window = 5 # according to Dudler et al they used 5 day realized volatility 

# Iterate over the headers and apply the moving averages
for header in new_headers:
    if header in dfs:
        # Calculate the short-window moving average and add it as a new column
        RAMOM_Signal_Return = dfs[header]['Log Returns'].rolling(window=RAMOM_return_window).sum()
        RAMOM_Signal_Volatility = dfs[header]['Log Returns'].rolling(window=RAMOM_volatlity_window).std()
        dfs[header]['Risk_adjusted_momentum'] = RAMOM_Signal_Return / RAMOM_Signal_Volatility
        dfs[header][f'RAMOM_{RAMOM_window_month}'] = dfs[header]['Risk_adjusted_momentum'].rolling(window=RAMOM_window).sum()
    else:
        print(f"DataFrame for {header} not found in dfs.")


# Iterate over the headers and apply the moving averages
for header in new_headers:
    if header in dfs:
        # Generate signals
        dfs[header][f'Signal_RAMOM_{RAMOM_window_month}'] = 0
        # Buy signal when
        dfs[header][f'Signal_RAMOM_{RAMOM_window_month}'] = np.where(dfs[header][f'RAMOM_{RAMOM_window_month}'] > 0, 1, dfs[header][f'Signal_RAMOM_{RAMOM_window_month}'])
        # Sell signal
        dfs[header][f'Signal_RAMOM_{RAMOM_window_month}'] = np.where(dfs[header][f'RAMOM_{RAMOM_window_month}'] < 0, -1, dfs[header][f'Signal_RAMOM_{RAMOM_window_month}'])

        # Generate trading orders
        # Conditions
        Buy = dfs[header][f'Signal_RAMOM_{RAMOM_window_month}'].diff() > 0
        Sell = dfs[header][f'Signal_RAMOM_{RAMOM_window_month}'].diff() < 0

        # Choices based on conditions
        choices = [1, -1]

        # Use np.select to apply conditions and choices
        dfs[header][f'Trades_RAMOM_{RAMOM_window_month}'] = np.select([Buy, Sell], choices, default=0)
        
    else:
        print(f"DataFrame for {header} not found in dfs.")


# Iterate over the headers and apply the moving averages
for header in new_headers:
    if header in dfs:
        # Initialize the Kalman filter
        kf = KalmanFilter(initial_state_mean=0,
                          initial_state_covariance=1,
                          observation_covariance=1,
                          transition_covariance=1,
                          transition_matrices=[1],
                          observation_matrices=[1])

        # Use the observed 'Last Price' as the measurement
        state_means, state_covariances = kf.filter(dfs[header][header])

        # The `state_means` variable contains the filtered state estimates
        dfs[header]['Kalman_Filter_Price'] = state_means
        
    else:
        print(f"DataFrame for {header} not found in dfs.")


# Iterate over the headers and apply the moving averages
for header in new_headers:
    if header in dfs:
        # Generate signals
        dfs[header]['Signal_Kalman_Filter'] = 0
        # Buy signal (Oversold)
        dfs[header]['Signal_Kalman_Filter'] = np.where(dfs[header][header] < dfs[header]['Kalman_Filter_Price'], 1, dfs[header]['Signal_Kalman_Filter'])
        # Sell signal (Overbought)
        dfs[header]['Signal_Kalman_Filter'] = np.where(dfs[header][header] > dfs[header]['Kalman_Filter_Price'], -1, dfs[header]['Signal_Kalman_Filter'])

        # Generate trading orders
        # Conditions
        Buy = dfs[header]['Signal_Kalman_Filter'].diff() > 0
        Sell = dfs[header]['Signal_Kalman_Filter'].diff() < 0

        # Choices based on conditions
        choices = [1, -1]

        # Use np.select to apply conditions and choices
        dfs[header]['Trades_Kalman_Filter'] = np.select([Buy, Sell], choices, default=0)
        
    else:
        print(f"DataFrame for {header} not found in dfs.")


# when do we want to start our strategy, so we can slize the dataframe
strategy_start = '2000-01-01'

# SMA
strategy_SMA = {}  # This will store the strategy DataFrames

for header in new_headers:  # Ensure new_headers is a list of columns in Futures_orig you're interested in
    # Initialize the strategy SMA DataFrame
    df_strategy_SMA = pd.DataFrame(index=dfs[header].index)
    df_strategy_SMA[f'{header}_Price'] = dfs[header][header]
    df_strategy_SMA['Log_Return'] = dfs[header]['Log Returns']
    df_strategy_SMA['Simple_Return'] = dfs[header]['Daily Returns']
    df_strategy_SMA[f'SMA_{short_SMA}'] = dfs[header][f'SMA_{short_SMA}']
    df_strategy_SMA[f'SMA_{long_SMA}'] = dfs[header][f'SMA_{long_SMA}']
    df_strategy_SMA['Signal_SMA_shifted'] = dfs[header]['Signal_SMA'].shift(1)
    df_strategy_SMA['Trades_SMA'] = dfs[header]['Trades_SMA'].shift(1)
    df_strategy_SMA['Cumulative_Log_Returns'] = float(0)
    
     # Convert the index to datetime format
    df_strategy_SMA.index = pd.to_datetime(df_strategy_SMA.index)
    
    # Store this strategy DataFrame in the dictionary
    strategy_SMA[header] = df_strategy_SMA

    
    
    
# SMA_bands
strategy_SMA_bands = {}  # This will store the strategy DataFrames

for header in new_headers:  # Ensure new_headers is a list of columns in Futures_orig you're interested in
    # Initialize the strategy SMA DataFrame
    df_strategy_SMA_bands = pd.DataFrame(index=dfs[header].index)
    df_strategy_SMA_bands[f'{header}_Price'] = dfs[header][header]
    df_strategy_SMA_bands['Log_Return'] = dfs[header]['Log Returns']
    df_strategy_SMA_bands['Simple_Return'] = dfs[header]['Daily Returns']
    df_strategy_SMA_bands[f'SMA_{short_SMA_with_Band}_with_Band'] = dfs[header][f'SMA_{short_SMA_with_Band}_with_Band']
    df_strategy_SMA_bands[f'SMA_{long_SMA_with_Band}_with_Band'] = dfs[header][f'SMA_{long_SMA_with_Band}_with_Band']
    df_strategy_SMA_bands['SMA_with_Band_UpperBand'] = dfs[header]['SMA_with_Band_UpperBand']
    df_strategy_SMA_bands['SMA_with_Band_LowerBand'] = dfs[header]['SMA_with_Band_LowerBand']
    df_strategy_SMA_bands['Signal_SMA_with_Band_shifted'] = dfs[header]['Signal_SMA_with_Band'].shift(1)
    df_strategy_SMA_bands['Trades_SMA_with_Band'] = dfs[header]['Trades_SMA_with_Band'].shift(1)
    df_strategy_SMA_bands['Cumulative_Log_Returns'] = float(0)
    
     # Convert the index to datetime format
    df_strategy_SMA_bands.index = pd.to_datetime(df_strategy_SMA_bands.index)
    
    # Store this strategy DataFrame in the dictionary
    strategy_SMA_bands[header] = df_strategy_SMA_bands  
    

    
    
# TSMOM
strategy_TSMOM = {}  # This will store the strategy DataFrames

for header in new_headers:  # Ensure new_headers is a list of columns in Futures_orig you're interested in
    # Initialize the strategy SMA DataFrame
    df_strategy_TSMOM = pd.DataFrame(index=dfs[header].index)
    df_strategy_TSMOM[f'{header}_Price'] = dfs[header][header]
    df_strategy_TSMOM['Log_Return'] = dfs[header]['Log Returns']
    df_strategy_TSMOM['Simple_Return'] = dfs[header]['Daily Returns']
    df_strategy_TSMOM[f'TSMOM_{TSMOM_window_month}'] = dfs[header][f'TSMOM_{TSMOM_window_month}']
    df_strategy_TSMOM[f'Signal_TSMOM_{TSMOM_window_month}_shifted'] = dfs[header][f'Signal_TSMOM_{TSMOM_window_month}'].shift(1)
    df_strategy_TSMOM[f'Trades_TSMOM_{TSMOM_window_month}'] = dfs[header][f'Trades_TSMOM_{TSMOM_window_month}'].shift(1)
    df_strategy_TSMOM['Cumulative_Log_Returns'] = float(0)
    
     # Convert the index to datetime format
    df_strategy_TSMOM.index = pd.to_datetime(df_strategy_TSMOM.index)
    
    # Store this strategy DataFrame in the dictionary
    strategy_TSMOM[header] = df_strategy_TSMOM  
    

    
    
# RAMOM
strategy_RAMOM = {}  # This will store the strategy DataFrames

for header in new_headers:  # Ensure new_headers is a list of columns in Futures_orig you're interested in
    # Initialize the strategy SMA DataFrame
    df_strategy_RAMOM = pd.DataFrame(index=dfs[header].index)
    df_strategy_RAMOM[f'{header}_Price'] = dfs[header][header]
    df_strategy_RAMOM['Log_Return'] = dfs[header]['Log Returns']
    df_strategy_RAMOM['Simple_Return'] = dfs[header]['Daily Returns']
    df_strategy_RAMOM['Risk_adjusted_momentum'] = dfs[header]['Risk_adjusted_momentum']
    df_strategy_RAMOM[f'RAMOM_{RAMOM_window_month}'] = dfs[header][f'RAMOM_{RAMOM_window_month}']
    df_strategy_RAMOM[f'Signal_RAMOM_{RAMOM_window_month}_shifted'] = dfs[header][f'Signal_RAMOM_{RAMOM_window_month}'].shift(1)
    df_strategy_RAMOM[f'Trades_RAMOM_{RAMOM_window_month}'] = dfs[header][f'Trades_RAMOM_{RAMOM_window_month}'].shift(1)
    df_strategy_RAMOM['Cumulative_Log_Returns'] = float(0)

     # Convert the index to datetime format
    df_strategy_RAMOM.index = pd.to_datetime(df_strategy_RAMOM.index)
    
    # Store this strategy DataFrame in the dictionary
    strategy_RAMOM[header] = df_strategy_RAMOM  
    
    
    
    
# Kalman
strategy_kalman = {}  # This will store the strategy DataFrames

for header in new_headers:  # Ensure new_headers is a list of columns in Futures_orig you're interested in
    # Initialize the strategy SMA DataFrame
    df_strategy_kalman = pd.DataFrame(index=dfs[header].index)
    df_strategy_kalman[f'{header}_Price'] = dfs[header][header]
    df_strategy_kalman['Log_Return'] = dfs[header]['Log Returns']
    df_strategy_kalman['Simple_Return'] = dfs[header]['Daily Returns']
    df_strategy_kalman['Kalman_Filter_Price'] = dfs[header]['Kalman_Filter_Price']
    df_strategy_kalman['Signal_Kalman_Filter_shifted'] = dfs[header]['Signal_Kalman_Filter'].shift(1)
    df_strategy_kalman['Trades_Kalman_Filter'] = dfs[header]['Trades_Kalman_Filter'].shift(1)
    df_strategy_kalman['Cumulative_Log_Returns'] = float(0)
    
     # Convert the index to datetime format
    df_strategy_kalman.index = pd.to_datetime(df_strategy_kalman.index)
    
    # Store this strategy DataFrame in the dictionary
    strategy_kalman[header] = df_strategy_kalman


strategy_start = '2000-01-01'
start_date = strategy_start
initial_investment = 1000000


#initial_investment = 1000000
#strategy_start = '2000-01-01'

for header in new_headers:
    # Running total of log returns based on trade signals
    running_total = float(0)

    # Find the next available date in the DataFrame index closest to strategy_start
    start_date_index = strategy_SMA[header].index.get_loc(pd.to_datetime(strategy_start), method='backfill')
    start_date = strategy_SMA[header].index[start_date_index]

    for date, row in strategy_SMA[header].iterrows():
        # Skip the rows before the adjusted strategy start date
        if date < start_date:
            continue

        # Update Trades only on the adjusted strategy start date
        if date == start_date:
            strategy_SMA[header].loc[date, 'Trades_SMA'] = row['Signal_SMA_shifted']

        # Update running total based on trade signals
        if row['Signal_SMA_shifted'] == 1:  # Buy signal
            running_total += row['Log_Return']
        elif row['Signal_SMA_shifted'] == -1:  # Sell signal
            running_total -= row['Log_Return']
        
        # Calculate cumulative returns
        cumulative_log_return = np.exp(running_total) - 1 
        
        # Store the running total and the cumulative returns in the DataFrame
        strategy_SMA[header].loc[date, 'Cumulative_Log_Returns'] = running_total
        strategy_SMA[header].loc[date, 'Total_Log_Return'] = initial_investment * (1 + cumulative_log_return)

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-27-ac3db5de6a92>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.


what_to_plot = 'Zinc'
#start_date = '2000-01-01'

# Filter the DataFrame to include only data from the start_date onwards
filtered_data = strategy_SMA[what_to_plot][strategy_SMA[what_to_plot].index >= pd.to_datetime(start_date)]

# Creating the trace for the current feature from the filtered data
trace = go.Scatter(x=filtered_data.index, y=filtered_data['Cumulative_Log_Returns'], mode='lines', name='Cumulative Log Returns')

# Creating the data list with the current trace
data = [trace]

# Creating the layout for the current plot, dynamically setting the title to the current feature
layout = go.Layout(title=f"{what_to_plot} - SMA Strategy over Time", xaxis=dict(title='Days'), yaxis=dict(title='Value'))

# Creating the figure with the current data and layout
fig = go.Figure(data=data, layout=layout)

# Displaying the figure
fig.show()


#what_to_plot = 'WTI Crude Oil'
#start_date = '2000-01-01' 

# Filter the DataFrame to include only data from the start_date onwards
filtered_data = strategy_SMA[what_to_plot][strategy_SMA[what_to_plot].index >= pd.to_datetime(start_date)]

# Calculate the number of trades from the filtered data
no_trades = filtered_data['Trades_SMA'].abs().sum()
print(f"Number of Trades: {int(no_trades)}")

# Calculate the minimum total log return from the filtered data
min_strategy = filtered_data['Total_Log_Return'].min()
day_min = filtered_data['Total_Log_Return'].idxmin()
print(f"Min per strategy: {min_strategy:,.2f}")
print(f"Day with min: {day_min.strftime('%Y-%m-%d')}")

# Calculate the maximum total log return from the filtered data
max_strategy = filtered_data['Total_Log_Return'].max()
day_max = filtered_data['Total_Log_Return'].idxmax()
print(f"Max per strategy: {max_strategy:,.2f}")
print(f"Day with max: {day_max.strftime('%Y-%m-%d')}")

Number of Trades: 163
Min per strategy: 885,229.94
Day with min: 2000-10-11
Max per strategy: 13,953,827.37
Day with max: 2022-04-19


# Filter the DataFrame to only include data from the start_date onwards
filtered_data = strategy_SMA[what_to_plot][strategy_SMA[what_to_plot].index >= pd.to_datetime(start_date)]

# Create a figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add the closing price trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data[f'{what_to_plot}_Price'], mode='lines', name='Closing price'),
    secondary_y=False,
)

# Add the short moving average trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data[f'SMA_{short_SMA}'], mode='lines', name=f'{short_SMA}-day MA'),
    secondary_y=False,
)

# Add the long moving average trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data[f'SMA_{long_SMA}'], mode='lines', name=f'{long_SMA}-day MA'),
    secondary_y=False,
)

# Filter for buy signals and create a trace
fig.add_trace(
    go.Scatter(
        x=filtered_data[filtered_data['Trades_SMA'] == 1].index,
        y=filtered_data[filtered_data['Trades_SMA'] == 1][f'{what_to_plot}_Price'],
        mode='markers',
        name='Buy signal',
        marker=dict(
            color='green',
            size=10,
            symbol='triangle-up'
        )
    ),
    secondary_y=False,
)

# Filter for sell signals and create a trace
fig.add_trace(
    go.Scatter(
        x=filtered_data[filtered_data['Trades_SMA'] == -1].index,
        y=filtered_data[filtered_data['Trades_SMA'] == -1][f'{what_to_plot}_Price'],
        mode='markers',
        name='Sell signal',
        marker=dict(
            color='red',
            size=10,
            symbol='triangle-down'
        )
    ),
    secondary_y=False,
)

# Add the Total Portfolio trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data['Total_Log_Return'], mode='lines', name='Portfolio value', line=dict(color='rgb(44, 160, 44)')),
    secondary_y=True,
)

# Add figure title with custom font settings
fig.update_layout(
    title={
        'text': f'{what_to_plot} - SMA strategy',
        'y':0.9,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top',
        'font': dict(size=18, color='black', family='Arial')
    },
    plot_bgcolor='white',
    paper_bgcolor='white',
    xaxis=dict(
        showgrid=True,
        gridcolor='lightgrey',
        tickangle=-45  # Rotate the x-axis labels by 45 degrees
    ),
    yaxis=dict(
        showgrid=True,
        gridcolor='lightgrey'
    ),
    yaxis2=dict(
        showgrid=True,
        gridcolor='lightgrey'
    )
)

# Set y-axes titles
fig.update_yaxes(title_text=f'{what_to_plot} price', secondary_y=False)
fig.update_yaxes(title_text='Portfolio value', secondary_y=True)

# Show the figure
fig.show()


#initial_investment = 1000000
#strategy_start = '2000-01-01'

for header in new_headers:
    # Running total of log returns based on trade signals
    running_total = float(0)

    # Find the next available date in the DataFrame index closest to strategy_start
    start_date_index = strategy_SMA_bands[header].index.get_loc(pd.to_datetime(strategy_start), method='backfill')
    start_date = strategy_SMA_bands[header].index[start_date_index]
    
    for date, row in strategy_SMA_bands[header].iterrows():
        # Skip the rows before the adjusted strategy start date
        if date < start_date:
            continue

        # Update Trades only on the adjusted strategy start date
        if date == start_date:
            strategy_SMA_bands[header].loc[date, 'Trades_SMA_with_Band'] = row['Signal_SMA_with_Band_shifted']        

        # Update running total based on trade signals
        if row['Signal_SMA_with_Band_shifted'] == 1:  # Buy signal
            running_total += row['Log_Return']
        elif row['Signal_SMA_with_Band_shifted'] == -1:  # Sell signal
            running_total -= row['Log_Return']
            
        # Calculate cumulative returns
        cumulative_log_return = np.exp(running_total) - 1 
            
        # Store the running total in the Cumulative_Returns column of the same DataFrame
        strategy_SMA_bands[header].loc[date, 'Cumulative_Log_Returns'] = running_total
        strategy_SMA_bands[header].loc[date, 'Total_Log_Return'] = initial_investment * (1 + cumulative_log_return)

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-31-68602d2f75c2>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.


what_to_plot = 'Zinc'
# start_date = '2000-01-01'

# Filter the DataFrame to include only data from the start_date onwards
filtered_data = strategy_SMA_bands[what_to_plot][strategy_SMA_bands[what_to_plot].index >= pd.to_datetime(start_date)]

# Creating the trace for the current feature from the filtered data
trace = go.Scatter(
    x=filtered_data.index, 
    y=filtered_data['Total_Log_Return'], 
    mode='lines', 
    name='Total Log Return'
)

# Creating the data list with the current trace
data = [trace]

# Creating the layout for the current plot, dynamically setting the title to the current feature
layout = go.Layout(
    title=f"{what_to_plot} - SMA with Bands Strategy over Time", 
    xaxis=dict(title='Days'), 
    yaxis=dict(title='Value')
)

# Creating the figure with the current data and layout
fig = go.Figure(data=data, layout=layout)

# Displaying the figure
fig.show()


#what_to_plot = 'WTI Crude Oil'
#start_date = '2000-01-01' 

# Filter the DataFrame to include only data from the start_date onwards
filtered_data = strategy_SMA_bands[what_to_plot][strategy_SMA_bands[what_to_plot].index >= pd.to_datetime(start_date)]

# Calculate the number of trades from the filtered data
no_trades = filtered_data['Trades_SMA_with_Band'].abs().sum()
print(f"Number of Trades: {int(no_trades)}")

# Calculate the minimum total log return from the filtered data
min_strategy = filtered_data['Total_Log_Return'].min()
day_min = filtered_data['Total_Log_Return'].idxmin()
print(f"Min per strategy: {min_strategy:,.2f}")
print(f"Day with min: {day_min.strftime('%Y-%m-%d')}")

# Calculate the maximum total log return from the filtered data
max_strategy = filtered_data['Total_Log_Return'].max()
day_max = filtered_data['Total_Log_Return'].idxmax()
print(f"Max per strategy: {max_strategy:,.2f}")
print(f"Day with max: {day_max.strftime('%Y-%m-%d')}")

Number of Trades: 91
Min per strategy: 843,142.84
Day with min: 2000-10-11
Max per strategy: 11,062,789.32
Day with max: 2022-04-19


# Filter the DataFrame to only include data from the start_date onwards
filtered_data = strategy_SMA_bands[what_to_plot][strategy_SMA_bands[what_to_plot].index >= pd.to_datetime(start_date)]

# Create a figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add the closing price trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data[f'{what_to_plot}_Price'], mode='lines', name='Closing price'),
    secondary_y=False,
)

# Add the upper band trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data['SMA_with_Band_UpperBand'], mode='lines', name='Upper band'),
    secondary_y=False,
)

# Add the lower band trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data['SMA_with_Band_LowerBand'], mode='lines', name='Lower band'),
    secondary_y=False,
)

# Filter for buy signals and create a trace
fig.add_trace(
    go.Scatter(
        x=filtered_data[filtered_data['Trades_SMA_with_Band'] == 1].index,
        y=filtered_data[filtered_data['Trades_SMA_with_Band'] == 1][f'{what_to_plot}_Price'],
        mode='markers',
        name='Buy signal',
        marker=dict(
            color='green',
            size=10,
            symbol='triangle-up'
        )
    ),
    secondary_y=False,
)

# Filter for sell signals and create a trace
fig.add_trace(
    go.Scatter(
        x=filtered_data[filtered_data['Trades_SMA_with_Band'] == -1].index,
        y=filtered_data[filtered_data['Trades_SMA_with_Band'] == -1][f'{what_to_plot}_Price'],
        mode='markers',
        name='Sell signal',
        marker=dict(
            color='red',
            size=10,
            symbol='triangle-down'
        )
    ),
    secondary_y=False,
)

# Add the Total Portfolio trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data['Total_Log_Return'], mode='lines', name='Portfolio value', line=dict(color='rgb(44, 160, 44)')),
    secondary_y=True,
)

# Add figure title with custom font settings
fig.update_layout(
    title={
        'text': f'{what_to_plot} - SMA with bands strategy',
        'y':0.9,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top',
        'font': dict(size=18, color='black', family='Arial')
    },
    plot_bgcolor='white',
    paper_bgcolor='white',
    xaxis=dict(
        showgrid=True,
        gridcolor='lightgrey',
        tickangle=-45  # Rotate the x-axis labels by 45 degrees
    ),
    yaxis=dict(
        showgrid=True,
        gridcolor='lightgrey'
    ),
    yaxis2=dict(
        showgrid=True,
        gridcolor='lightgrey'
    )
)

# Set y-axes titles
fig.update_yaxes(title_text=f'{what_to_plot} price', secondary_y=False)
fig.update_yaxes(title_text='Portfolio value', secondary_y=True)

# Show the figure
fig.show()


#initial_investment = 1000000
#strategy_start = '2000-01-01'

for header in new_headers:
    # Running total of log returns based on trade signals
    running_total = float(0)
    
    # Find the next available date in the DataFrame index closest to strategy_start
    start_date_index = strategy_TSMOM[header].index.get_loc(pd.to_datetime(strategy_start), method='backfill')
    start_date = strategy_TSMOM[header].index[start_date_index]

    for date, row in strategy_TSMOM[header].iterrows():
        # Skip the rows before the adjusted strategy start date
        if date < start_date:
            continue

        # Update Trades only on the adjusted strategy start date
        if date == start_date:
            strategy_TSMOM[header].loc[date, f'Trades_TSMOM_{TSMOM_window_month}'] = row[f'Signal_TSMOM_{TSMOM_window_month}_shifted']
        
        # Update running total based on trade signals
        if row[f'Signal_TSMOM_{TSMOM_window_month}_shifted'] == 1:  # Buy signal
            running_total += row['Log_Return']
        elif row[f'Signal_TSMOM_{TSMOM_window_month}_shifted'] == -1:  # Sell signal
            running_total -= row['Log_Return']
            
        # Calculate cumulative returns
        cumulative_log_return = np.exp(running_total) - 1 
            
        # Store the running total in the Cumulative_Returns column of the same DataFrame
        strategy_TSMOM[header].loc[date, 'Cumulative_Log_Returns'] = running_total
        strategy_TSMOM[header].loc[date, 'Total_Log_Return'] = initial_investment * (1 + cumulative_log_return)

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-35-c6477c451acb>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.


#what_to_plot = 'Zinc'
#start_date = '2000-01-01' 

# Filter the DataFrame to include only data from the start_date onwards
filtered_data = strategy_TSMOM[what_to_plot][strategy_TSMOM[what_to_plot].index >= pd.to_datetime(start_date)]

# Creating the trace for the current feature from the filtered data
trace = go.Scatter(
    x=filtered_data.index, 
    y=filtered_data['Total_Log_Return'], 
    mode='lines', 
    name='Total Log Return'
)

# Creating the data list with the current trace
data = [trace]

# Creating the layout for the current plot, dynamically setting the title to the current feature
layout = go.Layout(
    title=f"{what_to_plot} - TSMOM Strategy over Time", 
    xaxis=dict(title='Days'), 
    yaxis=dict(title='Value')
)

# Creating the figure with the current data and layout
fig = go.Figure(data=data, layout=layout)

# Displaying the figure
fig.show()


#what_to_plot = 'WTI Crude Oil'
#start_date = '2000-01-01' 

# Filter the DataFrame to include only data from the start_date onwards
filtered_data = strategy_TSMOM[what_to_plot][strategy_TSMOM[what_to_plot].index >= pd.to_datetime(start_date)]

# Calculate the number of trades from the filtered data
no_trades = filtered_data[f'Trades_TSMOM_{TSMOM_window_month}'].abs().sum()
print(f"Number of Trades: {int(no_trades)}")

# Calculate the minimum total log return from the filtered data
min_strategy = filtered_data['Total_Log_Return'].min()
day_min = filtered_data['Total_Log_Return'].idxmin()
print(f"Min per strategy: {min_strategy:,.2f}")
print(f"Day with min: {day_min.strftime('%Y-%m-%d')}")

# Calculate the maximum total log return from the filtered data
max_strategy = filtered_data['Total_Log_Return'].max()
day_max = filtered_data['Total_Log_Return'].idxmax()
print(f"Max per strategy: {max_strategy:,.2f}")
print(f"Day with max: {day_max.strftime('%Y-%m-%d')}")

Number of Trades: 150
Min per strategy: 618,720.08
Day with min: 2000-09-12
Max per strategy: 11,291,071.96
Day with max: 2008-12-12


# Filter the DataFrame to only include data from the start_date onwards
filtered_data = strategy_TSMOM[what_to_plot][strategy_TSMOM[what_to_plot].index >= pd.to_datetime(start_date)]

# Create a figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add the closing price trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data[f'{what_to_plot}_Price'], mode='lines', name='Closing price'),
    secondary_y=False,
)

# Filter for buy signals and create a trace
fig.add_trace(
    go.Scatter(
        x=filtered_data[filtered_data[f'Trades_TSMOM_{TSMOM_window_month}'] == 1].index,
        y=filtered_data[filtered_data[f'Trades_TSMOM_{TSMOM_window_month}'] == 1][f'{what_to_plot}_Price'],
        mode='markers',
        name='Buy signal',
        marker=dict(
            color='green',
            size=10,
            symbol='triangle-up'
        )
    ),
    secondary_y=False,
)

# Filter for sell signals and create a trace
fig.add_trace(
    go.Scatter(
        x=filtered_data[filtered_data[f'Trades_TSMOM_{TSMOM_window_month}'] == -1].index,
        y=filtered_data[filtered_data[f'Trades_TSMOM_{TSMOM_window_month}'] == -1][f'{what_to_plot}_Price'],
        mode='markers',
        name='Sell signal',
        marker=dict(
            color='red',
            size=10,
            symbol='triangle-down'
        )
    ),
    secondary_y=False,
)

# Add the Total Portfolio trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data['Total_Log_Return'], mode='lines', name='Portfolio value', line=dict(color='rgb(44, 160, 44)')),
    secondary_y=True,
)

# Add figure title with custom font settings
fig.update_layout(
    title={
        'text': f'{what_to_plot} - TSMOM strategy',
        'y':0.9,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top',
        'font': dict(size=18, color='black', family='Arial')
    },
    plot_bgcolor='white',
    paper_bgcolor='white',
    xaxis=dict(
        showgrid=True,
        gridcolor='lightgrey',
        tickangle=-45  # Rotate the x-axis labels by 45 degrees
    ),
    yaxis=dict(
        showgrid=True,
        gridcolor='lightgrey'
    ),
    yaxis2=dict(
        showgrid=True,
        gridcolor='lightgrey'
    )
)

# Set y-axes titles
fig.update_yaxes(title_text=f'{what_to_plot} price', secondary_y=False)
fig.update_yaxes(title_text='Portfolio value', secondary_y=True)

# Show the figure
fig.show()


#initial_investment = 1000000
#strategy_start = '2000-01-01'

for header in new_headers:
    # Running total of log returns based on trade signals
    running_total = float(0)
    
    # Find the next available date in the DataFrame index closest to strategy_start
    start_date_index = strategy_RAMOM[header].index.get_loc(pd.to_datetime(strategy_start), method='backfill')
    start_date = strategy_RAMOM[header].index[start_date_index]

    for date, row in strategy_RAMOM[header].iterrows():
        # Skip the rows before the adjusted strategy start date
        if date < start_date:
            continue

        # Update Trades only on the adjusted strategy start date
        if date == start_date:
            strategy_RAMOM[header].loc[date, f'Trades_RAMOM_{RAMOM_window_month}'] = row[f'Signal_RAMOM_{RAMOM_window_month}_shifted']
            
        # Update running total based on trade signals
        if row[f'Signal_RAMOM_{RAMOM_window_month}_shifted'] == 1:  # Buy signal
            running_total += row['Log_Return']
        elif row[f'Signal_RAMOM_{RAMOM_window_month}_shifted'] == -1:  # Sell signal
            running_total -= row['Log_Return']
            
        # Calculate cumulative returns
        cumulative_log_return = np.exp(running_total) - 1 
            
        # Store the running total in the Cumulative_Returns column of the same DataFrame
        strategy_RAMOM[header].loc[date, 'Cumulative_Log_Returns'] = running_total
        strategy_RAMOM[header].loc[date, 'Total_Log_Return'] = initial_investment * (1 + cumulative_log_return)

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-39-e89eba58ffa3>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.


#what_to_plot = 'Zinc'
#start_date = '2000-01-01' 

# Filter the DataFrame to only include data from the start_date onwards
filtered_data = strategy_RAMOM[what_to_plot][strategy_RAMOM[what_to_plot].index >= pd.to_datetime(start_date)]

# Creating the trace for the current feature from the filtered data
trace = go.Scatter(
    x=filtered_data.index, 
    y=filtered_data['Total_Log_Return'], 
    mode='lines', 
    name='Total Log Return'
)

# Creating the data list with the current trace
data = [trace]

# Creating the layout for the current plot, dynamically setting the title to the current feature
layout = go.Layout(
    title=f"{what_to_plot} - RAMOM Strategy over Time", 
    xaxis=dict(title='Days'), 
    yaxis=dict(title='Value')
)

# Creating the figure with the current data and layout
fig = go.Figure(data=data, layout=layout)

# Displaying the figure
fig.show()


#what_to_plot = 'WTI Crude Oil'
#start_date = '2000-01-01' 

# Filter the DataFrame to include only data from the start_date onwards
filtered_data = strategy_RAMOM[what_to_plot][strategy_RAMOM[what_to_plot].index >= pd.to_datetime(start_date)]

# Calculate the number of trades from the filtered data
no_trades = filtered_data[f'Trades_RAMOM_{RAMOM_window_month}'].abs().sum()
print(f"Number of Trades: {int(no_trades)}")

# Calculate the minimum total log return from the filtered data
min_strategy = filtered_data['Total_Log_Return'].min()
day_min = filtered_data['Total_Log_Return'].idxmin()
print(f"Min per strategy: {min_strategy:,.2f}")
print(f"Day with min: {day_min.strftime('%Y-%m-%d')}")

# Calculate the maximum total log return from the filtered data
max_strategy = filtered_data['Total_Log_Return'].max()
day_max = filtered_data['Total_Log_Return'].idxmax()
print(f"Max per strategy: {max_strategy:,.2f}")
print(f"Day with max: {day_max.strftime('%Y-%m-%d')}")

Number of Trades: 156
Min per strategy: 828,911.37
Day with min: 2000-08-09
Max per strategy: 5,758,511.77
Day with max: 2008-11-07


# Filter the DataFrame to only include data from the start_date onwards
filtered_data = strategy_RAMOM[what_to_plot][strategy_RAMOM[what_to_plot].index >= pd.to_datetime(start_date)]

# Create a figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add the closing price trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data[f'{what_to_plot}_Price'], mode='lines', name='Closing price'),
    secondary_y=False,
)

# Filter for buy signals and create a trace
fig.add_trace(
    go.Scatter(
        x=filtered_data[filtered_data[f'Trades_RAMOM_{RAMOM_window_month}'] == 1].index,
        y=filtered_data[filtered_data[f'Trades_RAMOM_{RAMOM_window_month}'] == 1][f'{what_to_plot}_Price'],
        mode='markers',
        name='Buy signal',
        marker=dict(
            color='green',
            size=10,
            symbol='triangle-up'
        )
    ),
    secondary_y=False,
)

# Filter for sell signals and create a trace
fig.add_trace(
    go.Scatter(
        x=filtered_data[filtered_data[f'Trades_RAMOM_{RAMOM_window_month}'] == -1].index,
        y=filtered_data[filtered_data[f'Trades_RAMOM_{RAMOM_window_month}'] == -1][f'{what_to_plot}_Price'],
        mode='markers',
        name='Sell signal',
        marker=dict(
            color='red',
            size=10,
            symbol='triangle-down'
        )
    ),
    secondary_y=False,
)

# Add the Total Portfolio trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data['Total_Log_Return'], mode='lines', name='Portfolio value', line=dict(color='rgb(44, 160, 44)')),
    secondary_y=True,
)

# Add figure title with custom font settings
fig.update_layout(
    title={
        'text': f'{what_to_plot} - RAMOM strategy',
        'y':0.9,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top',
        'font': dict(size=18, color='black', family='Arial')
    },
    plot_bgcolor='white',
    paper_bgcolor='white',
    xaxis=dict(
        showgrid=True,
        gridcolor='lightgrey',
        tickangle=-45  # Rotate the x-axis labels by 45 degrees
    ),
    yaxis=dict(
        showgrid=True,
        gridcolor='lightgrey'
    ),
    yaxis2=dict(
        showgrid=True,
        gridcolor='lightgrey'
    )
)

# Set y-axes titles
fig.update_yaxes(title_text=f'{what_to_plot} price', secondary_y=False)
fig.update_yaxes(title_text='Portfolio value', secondary_y=True)

# Show the figure
fig.show()


#initial_investment = 1000000
#strategy_start = '2000-01-01'

for header in new_headers:
    # Running total of log returns based on trade signals
    running_total = float(0)

    # Find the next available date in the DataFrame index closest to strategy_start
    start_date_index = strategy_kalman[header].index.get_loc(pd.to_datetime(strategy_start), method='backfill')
    start_date = strategy_kalman[header].index[start_date_index]

    for date, row in strategy_kalman[header].iterrows():
        # Skip the rows before the adjusted strategy start date
        if date < start_date:
            continue

        # Update Trades only on the adjusted strategy start date
        if date == start_date:
            strategy_kalman[header].loc[date, 'Trades_Kalman_Filter'] = row['Signal_Kalman_Filter_shifted']

        # Update running total based on trade signals
        if row['Signal_Kalman_Filter_shifted'] == 1:  # Buy signal
            running_total += row['Log_Return']
        elif row['Signal_Kalman_Filter_shifted'] == -1:  # Sell signal
            running_total -= row['Log_Return']
            
        # Calculate cumulative returns
        cumulative_log_return = np.exp(running_total) - 1 
            
        # Store the running total in the Cumulative_Returns column of the same DataFrame
        strategy_kalman[header].loc[date, 'Cumulative_Log_Returns'] = running_total
        strategy_kalman[header].loc[date, 'Total_Log_Return'] = initial_investment * (1 + cumulative_log_return)

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.

<ipython-input-43-8e74fa085888>:9: FutureWarning:

Passing method to DatetimeIndex.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead.


what_to_plot = 'Zinc'
start_date = '2000-01-01'

# Filter the DataFrame to only include data from the start_date onwards
filtered_data = strategy_kalman[what_to_plot][strategy_kalman[what_to_plot].index >= pd.to_datetime(start_date)]

# Creating the trace for the current feature from the filtered data
trace = go.Scatter(
    x=filtered_data.index, 
    y=filtered_data['Total_Log_Return'], 
    mode='lines', 
    name='Total Log Return'
)

# Creating the data list with the current trace
data = [trace]

# Creating the layout for the current plot, dynamically setting the title to the current feature
layout = go.Layout(
    title=f"{what_to_plot} - Kalman Filter Strategy over Time", 
    xaxis=dict(title='Days'), 
    yaxis=dict(title='Value')
)

# Creating the figure with the current data and layout
fig = go.Figure(data=data, layout=layout)

# Displaying the figure
fig.show()


#what_to_plot = 'WTI Crude Oil'
#start_date = '2000-01-01' 

# Filter the DataFrame to include only data from the start_date onwards
filtered_data = strategy_kalman[what_to_plot][strategy_kalman[what_to_plot].index >= pd.to_datetime(start_date)]

# Calculate the number of trades from the filtered data
no_trades = filtered_data['Trades_Kalman_Filter'].abs().sum()
print(f"Number of Trades: {int(no_trades)}")

# Calculate the minimum total log return from the filtered data
min_strategy = filtered_data['Total_Log_Return'].min()
day_min = filtered_data['Total_Log_Return'].idxmin()
print(f"Min per strategy: {min_strategy:,.2f}")
print(f"Day with min: {day_min.strftime('%Y-%m-%d')}")

# Calculate the maximum total log return from the filtered data
max_strategy = filtered_data['Total_Log_Return'].max()
day_max = filtered_data['Total_Log_Return'].idxmax()
print(f"Max per strategy: {max_strategy:,.2f}")
print(f"Day with max: {day_max.strftime('%Y-%m-%d')}")

Number of Trades: 2263
Min per strategy: 652,909.92
Day with min: 2008-01-03
Max per strategy: 1,794,610.97
Day with max: 2005-05-03


# Filter the DataFrame to only include data from the start_date onwards
filtered_data = strategy_kalman[what_to_plot][strategy_kalman[what_to_plot].index >= pd.to_datetime(start_date)]

# Create a figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add the closing price trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data[f'{what_to_plot}_Price'], mode='lines', name='Closing price'),
    secondary_y=False,
)

# Add the kalman filter price trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data['Kalman_Filter_Price'], mode='lines', name='Kalman filter price'),
    secondary_y=False,
)

# Filter for buy signals and create a trace
fig.add_trace(
    go.Scatter(
        x=filtered_data[filtered_data['Trades_Kalman_Filter'] == 1].index,
        y=filtered_data[filtered_data['Trades_Kalman_Filter'] == 1][f'{what_to_plot}_Price'],
        mode='markers',
        name='Buy signal',
        marker=dict(
            color='green',
            size=10,
            symbol='triangle-up'
        )
    ),
    secondary_y=False,
)

# Filter for sell signals and create a trace
fig.add_trace(
    go.Scatter(
        x=filtered_data[filtered_data['Trades_Kalman_Filter'] == -1].index,
        y=filtered_data[filtered_data['Trades_Kalman_Filter'] == -1][f'{what_to_plot}_Price'],
        mode='markers',
        name='Sell signal',
        marker=dict(
            color='red',
            size=10,
            symbol='triangle-down'
        )
    ),
    secondary_y=False,
)

# Add the Total Portfolio trace from the filtered data
fig.add_trace(
    go.Scatter(x=filtered_data.index, y=filtered_data['Total_Log_Return'], mode='lines', name='Portfolio value', line=dict(color='rgb(44, 160, 44)')),
    secondary_y=True,
)

# Add figure title with custom font settings
fig.update_layout(
    title={
        'text': f'{what_to_plot} - Kalman filter strategy',
        'y':0.9,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top',
        'font': dict(size=18, color='black', family='Arial')
    },
    plot_bgcolor='white',
    paper_bgcolor='white',
    xaxis=dict(
        showgrid=True,
        gridcolor='lightgrey',
        tickangle=-45  # Rotate the x-axis labels by 45 degrees
    ),
    yaxis=dict(
        showgrid=True,
        gridcolor='lightgrey'
    ),
    yaxis2=dict(
        showgrid=True,
        gridcolor='lightgrey'
    )
)

# Set y-axes titles
fig.update_yaxes(title_text=f'{what_to_plot} price', secondary_y=False)
fig.update_yaxes(title_text='Portfolio value', secondary_y=True)

# Show the figure
fig.show()


for header, df in strategy_SMA.items():
    print(f"strategy_SMA['{header}']")
    
print('-' * 40)
print('Nr. of Futures: ' + str(len(strategy_SMA)))

strategy_SMA['USD Future']
strategy_SMA['EUR Future (USD)']
strategy_SMA['JPY Future (USD)']
strategy_SMA['GBP Future (USD)']
strategy_SMA['GBP Future (EUR)']
strategy_SMA['CAD Future (USD)']
strategy_SMA['CHF Future (USD)']
strategy_SMA['S&P 500']
strategy_SMA['Nasdaq 100']
strategy_SMA['S&P Canada 60']
strategy_SMA['FTSE 100']
strategy_SMA['DAX']
strategy_SMA['SMI']
strategy_SMA['CAC - 40']
strategy_SMA['IBEX 35']
strategy_SMA['Euro Stoxx 50']
strategy_SMA['Nikkei 225']
strategy_SMA['Hang Seng Index']
strategy_SMA['US 2-year']
strategy_SMA['US 5-year']
strategy_SMA['US 10-year']
strategy_SMA['Long Gilt (10 year)']
strategy_SMA['Euro-Schatz-Futures (2 year)']
strategy_SMA['Euro-Bobl-Future (5 year)']
strategy_SMA['Euro-Bund-Future (10 year)']
strategy_SMA['Euro-Buxl-Future (30 year)']
strategy_SMA['Brent Crude Oil']
strategy_SMA['WTI Crude Oil']
strategy_SMA['Heating Oil']
strategy_SMA['Natural Gas']
strategy_SMA['Gold']
strategy_SMA['Silver']
strategy_SMA['Platinum']
strategy_SMA['Palladium']
strategy_SMA['Copper']
strategy_SMA['Nickel']
strategy_SMA['Zinc']
strategy_SMA['Lean hogs']
strategy_SMA['Live cattle']
strategy_SMA['Feeder cattle']
strategy_SMA['Wheat']
strategy_SMA['Corn']
strategy_SMA['Oat']
strategy_SMA['Soybeans']
strategy_SMA['Soybean oil']
strategy_SMA['Frozen orange juice']
strategy_SMA['Cocoa']
strategy_SMA['Coffee']
strategy_SMA['Sugar No.11']
strategy_SMA['Cotton No.2']
----------------------------------------
Nr. of Futures: 50


#start_date = '2000-01-01' 

# List to hold the data series
data_series_list_SMA = []

# Extract 'Cumulative_Log_Returns' and rename it uniquely
for name, df in strategy_SMA.items():
    if 'Cumulative_Log_Returns' in df.columns:
        # Filter the DataFrame to include only data from the start_date onwards
        filtered_df = df[df.index >= pd.to_datetime(start_date)]
        
        # Rename the series with its asset name and add to the list
        series = filtered_df['Cumulative_Log_Returns'].rename(name)
        data_series_list_SMA.append(series)

# Combine all the series into a single DataFrame
combined_df_SMA = pd.concat(data_series_list_SMA, axis=1, join='outer')

# Forward fill missing values
combined_df_SMA_filled = combined_df_SMA.ffill()

# Drop rows where any NaN values still exist (likely at the start)
combined_df_SMA_cleaned = combined_df_SMA_filled.dropna()


pd.set_option('display.max_columns', None)

combined_df_SMA_cleaned.head()


# Step 2: Calculate daily log returns from cumulative log returns
daily_log_returns_SMA = combined_df_SMA_cleaned.diff().fillna(0)

# Initialize a DataFrame to hold the adjusted weights
adjusted_weights = pd.DataFrame(index=daily_log_returns_SMA.index, columns=daily_log_returns_SMA.columns)

# Calculate weights dynamically each day using cumulative returns to check asset activity
for date, returns in combined_df_SMA_cleaned.iterrows():
    # Use the cumulative returns to determine active assets
    active_assets = returns[returns != 0].count()  # Count non-zero cumulative returns for the day
    if active_assets > 0:
        daily_weights = 1 / active_assets  # Equal weight divided by the number of active assets
    else:
        daily_weights = 0  # No active assets to invest in
    adjusted_weights.loc[date] = daily_weights

# Save the adjusted weights to an Excel file
adjusted_weights.to_excel('rebalanced_weights_equalweighted_SMA.xlsx')

# Step 3: Calculate portfolio returns using the dynamically adjusted weights
portfolio_SMA_log_returns = (daily_log_returns_SMA * adjusted_weights).sum(axis=1)
portfolio_SMA_simple_returns = np.exp(portfolio_SMA_log_returns) - 1
portfolio_SMA_cumulative_simple_returns = (1 + portfolio_SMA_simple_returns).cumprod() - 1

# Step 4: Plot the performance
trace = go.Scatter(x=combined_df_SMA_cleaned.index, y=portfolio_SMA_cumulative_simple_returns, mode='lines', name='Cumulative Simple Returns')
layout = go.Layout(title='Equal-weighted Portfolio SMA - Adjusted Over Time', xaxis=dict(title='Days'), yaxis=dict(title='Simple Return'))
fig = go.Figure(data=[trace], layout=layout)
fig.show()

<ipython-input-50-893fd8db71bd>:18: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


for header, df in strategy_SMA_bands.items():
    print(f"strategy_SMA_bands['{header}']")
print('-' * 40)
print('Nr. of Futures: ' + str(len(strategy_SMA_bands)))

strategy_SMA_bands['USD Future']
strategy_SMA_bands['EUR Future (USD)']
strategy_SMA_bands['JPY Future (USD)']
strategy_SMA_bands['GBP Future (USD)']
strategy_SMA_bands['GBP Future (EUR)']
strategy_SMA_bands['CAD Future (USD)']
strategy_SMA_bands['CHF Future (USD)']
strategy_SMA_bands['S&P 500']
strategy_SMA_bands['Nasdaq 100']
strategy_SMA_bands['S&P Canada 60']
strategy_SMA_bands['FTSE 100']
strategy_SMA_bands['DAX']
strategy_SMA_bands['SMI']
strategy_SMA_bands['CAC - 40']
strategy_SMA_bands['IBEX 35']
strategy_SMA_bands['Euro Stoxx 50']
strategy_SMA_bands['Nikkei 225']
strategy_SMA_bands['Hang Seng Index']
strategy_SMA_bands['US 2-year']
strategy_SMA_bands['US 5-year']
strategy_SMA_bands['US 10-year']
strategy_SMA_bands['Long Gilt (10 year)']
strategy_SMA_bands['Euro-Schatz-Futures (2 year)']
strategy_SMA_bands['Euro-Bobl-Future (5 year)']
strategy_SMA_bands['Euro-Bund-Future (10 year)']
strategy_SMA_bands['Euro-Buxl-Future (30 year)']
strategy_SMA_bands['Brent Crude Oil']
strategy_SMA_bands['WTI Crude Oil']
strategy_SMA_bands['Heating Oil']
strategy_SMA_bands['Natural Gas']
strategy_SMA_bands['Gold']
strategy_SMA_bands['Silver']
strategy_SMA_bands['Platinum']
strategy_SMA_bands['Palladium']
strategy_SMA_bands['Copper']
strategy_SMA_bands['Nickel']
strategy_SMA_bands['Zinc']
strategy_SMA_bands['Lean hogs']
strategy_SMA_bands['Live cattle']
strategy_SMA_bands['Feeder cattle']
strategy_SMA_bands['Wheat']
strategy_SMA_bands['Corn']
strategy_SMA_bands['Oat']
strategy_SMA_bands['Soybeans']
strategy_SMA_bands['Soybean oil']
strategy_SMA_bands['Frozen orange juice']
strategy_SMA_bands['Cocoa']
strategy_SMA_bands['Coffee']
strategy_SMA_bands['Sugar No.11']
strategy_SMA_bands['Cotton No.2']
----------------------------------------
Nr. of Futures: 50


#start_date = '2000-01-01'

# List to hold the data series
data_series_list_SMA_bands = []

# Extract 'Cumulative_Log_Returns' and rename it uniquely
for name, df in strategy_SMA_bands.items():
    if 'Cumulative_Log_Returns' in df.columns:
        # Filter the DataFrame to only include data from the start_date onwards
        filtered_df = df[df.index >= pd.to_datetime(start_date)]
        
        # Rename the series with its asset name and add to the list
        series = filtered_df['Cumulative_Log_Returns'].rename(name)
        data_series_list_SMA_bands.append(series)

# Combine all the series into a single DataFrame
combined_df_SMA_bands = pd.concat(data_series_list_SMA_bands, axis=1, join='outer')

# Forward fill missing values
combined_df_SMA_bands_filled = combined_df_SMA_bands.ffill()

# Drop rows where any NaN values still exist (likely at the start)
combined_df_SMA_bands_cleaned = combined_df_SMA_bands_filled.dropna()


pd.set_option('display.max_columns', None)

combined_df_SMA_bands_cleaned.head()


# Step 2: Calculate daily log returns from cumulative log returns
daily_log_returns_SMA_bands = combined_df_SMA_bands_cleaned.diff().fillna(0)

# Initialize a DataFrame to hold the adjusted weights
adjusted_weights = pd.DataFrame(index=daily_log_returns_SMA_bands.index, columns=daily_log_returns_SMA_bands.columns)

# Calculate weights dynamically each day using cumulative returns to check asset activity
for date, returns in combined_df_SMA_bands_cleaned.iterrows():
    # Use the cumulative returns to determine active assets
    active_assets = returns[returns != 0].count()  # Count non-zero cumulative returns for the day
    if active_assets > 0:
        daily_weights = 1 / active_assets  # Equal weight divided by the number of active assets
    else:
        daily_weights = 0  # No active assets to invest in
    adjusted_weights.loc[date] = daily_weights

# Save the adjusted weights to an Excel file
adjusted_weights.to_excel('rebalanced_weights_equalweighted_SMA_bands.xlsx')

# Step 3: Calculate portfolio returns using the dynamically adjusted weights
portfolio_SMA_bands_log_returns = (daily_log_returns_SMA_bands * adjusted_weights).sum(axis=1)
portfolio_SMA_bands_simple_returns = np.exp(portfolio_SMA_bands_log_returns) - 1
portfolio_SMA_bands_cumulative_simple_returns = (1 + portfolio_SMA_bands_simple_returns).cumprod() - 1

# Step 4: Plot the performance
trace = go.Scatter(x=combined_df_SMA_bands_cleaned.index, y=portfolio_SMA_bands_cumulative_simple_returns, mode='lines', name='Cumulative Simple Returns')
layout = go.Layout(title='Equal-weighted Portfolio SMA with Bands - Adjusted Over Time', xaxis=dict(title='Days'), yaxis=dict(title='Simple Return'))
fig = go.Figure(data=[trace], layout=layout)
fig.show()

<ipython-input-54-956381f5dd7a>:18: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


for header, df in strategy_TSMOM.items():
    print(f"strategy_TSMOM['{header}']")
print('-' * 40)
print('Nr. of Futures: ' + str(len(strategy_TSMOM)))

strategy_TSMOM['USD Future']
strategy_TSMOM['EUR Future (USD)']
strategy_TSMOM['JPY Future (USD)']
strategy_TSMOM['GBP Future (USD)']
strategy_TSMOM['GBP Future (EUR)']
strategy_TSMOM['CAD Future (USD)']
strategy_TSMOM['CHF Future (USD)']
strategy_TSMOM['S&P 500']
strategy_TSMOM['Nasdaq 100']
strategy_TSMOM['S&P Canada 60']
strategy_TSMOM['FTSE 100']
strategy_TSMOM['DAX']
strategy_TSMOM['SMI']
strategy_TSMOM['CAC - 40']
strategy_TSMOM['IBEX 35']
strategy_TSMOM['Euro Stoxx 50']
strategy_TSMOM['Nikkei 225']
strategy_TSMOM['Hang Seng Index']
strategy_TSMOM['US 2-year']
strategy_TSMOM['US 5-year']
strategy_TSMOM['US 10-year']
strategy_TSMOM['Long Gilt (10 year)']
strategy_TSMOM['Euro-Schatz-Futures (2 year)']
strategy_TSMOM['Euro-Bobl-Future (5 year)']
strategy_TSMOM['Euro-Bund-Future (10 year)']
strategy_TSMOM['Euro-Buxl-Future (30 year)']
strategy_TSMOM['Brent Crude Oil']
strategy_TSMOM['WTI Crude Oil']
strategy_TSMOM['Heating Oil']
strategy_TSMOM['Natural Gas']
strategy_TSMOM['Gold']
strategy_TSMOM['Silver']
strategy_TSMOM['Platinum']
strategy_TSMOM['Palladium']
strategy_TSMOM['Copper']
strategy_TSMOM['Nickel']
strategy_TSMOM['Zinc']
strategy_TSMOM['Lean hogs']
strategy_TSMOM['Live cattle']
strategy_TSMOM['Feeder cattle']
strategy_TSMOM['Wheat']
strategy_TSMOM['Corn']
strategy_TSMOM['Oat']
strategy_TSMOM['Soybeans']
strategy_TSMOM['Soybean oil']
strategy_TSMOM['Frozen orange juice']
strategy_TSMOM['Cocoa']
strategy_TSMOM['Coffee']
strategy_TSMOM['Sugar No.11']
strategy_TSMOM['Cotton No.2']
----------------------------------------
Nr. of Futures: 50


#start_date = '2000-01-01' 

# List to hold the data series
data_series_list_TSMOM = []

# Extract 'Cumulative_Log_Returns' and rename it uniquely
for name, df in strategy_TSMOM.items():
    if 'Cumulative_Log_Returns' in df.columns:
        # Filter the DataFrame to only include data from the start_date onwards
        filtered_df = df[df.index >= pd.to_datetime(start_date)]
        
        # Rename the series with its asset name and add to the list
        series = filtered_df['Cumulative_Log_Returns'].rename(name)
        data_series_list_TSMOM.append(series)

# Combine all the series into a single DataFrame
combined_df_TSMOM = pd.concat(data_series_list_TSMOM, axis=1, join='outer')

# Forward fill missing values
combined_df_TSMOM_filled = combined_df_TSMOM.ffill()

# Drop rows where any NaN values still exist (likely at the start)
combined_df_TSMOM_cleaned = combined_df_TSMOM_filled.dropna()


pd.set_option('display.max_columns', None)

combined_df_TSMOM_cleaned.head()


# Step 2: Calculate daily log returns from cumulative log returns
daily_log_returns_TSMOM = combined_df_TSMOM_cleaned.diff().fillna(0)

# Initialize a DataFrame to hold the adjusted weights
adjusted_weights = pd.DataFrame(index=daily_log_returns_TSMOM.index, columns=daily_log_returns_TSMOM.columns)

# Calculate weights dynamically each day using cumulative returns to check asset activity
for date, returns in combined_df_TSMOM_cleaned.iterrows():
    # Use the cumulative returns to determine active assets
    active_assets = returns[returns != 0].count()  # Count non-zero cumulative returns for the day
    if active_assets > 0:
        daily_weights = 1 / active_assets  # Equal weight divided by the number of active assets
    else:
        daily_weights = 0  # No active assets to invest in
    adjusted_weights.loc[date] = daily_weights

# Save the adjusted weights to an Excel file
adjusted_weights.to_excel('rebalanced_weights_equalweighted_TSMOM.xlsx')

# Step 3: Calculate portfolio returns using the dynamically adjusted weights
portfolio_TSMOM_log_returns = (daily_log_returns_TSMOM * adjusted_weights).sum(axis=1)
portfolio_TSMOM_simple_returns = np.exp(portfolio_TSMOM_log_returns) - 1
portfolio_TSMOM_cumulative_simple_returns = (1 + portfolio_TSMOM_simple_returns).cumprod() - 1

# Step 4: Plot the performance
trace = go.Scatter(x=combined_df_TSMOM_cleaned.index, y=portfolio_TSMOM_cumulative_simple_returns, mode='lines', name='Cumulative Simple Returns')
layout = go.Layout(title='Equal-weighted Portfolio TSMOM - Adjusted Over Time', xaxis=dict(title='Days'), yaxis=dict(title='Simple Return'))
fig = go.Figure(data=[trace], layout=layout)
fig.show()

<ipython-input-58-75b0ada3f235>:18: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


for header, df in strategy_RAMOM.items():
    print(f"strategy_RAMOM['{header}']")
print('-' * 40)
print('Nr. of Futures: ' + str(len(strategy_RAMOM)))

strategy_RAMOM['USD Future']
strategy_RAMOM['EUR Future (USD)']
strategy_RAMOM['JPY Future (USD)']
strategy_RAMOM['GBP Future (USD)']
strategy_RAMOM['GBP Future (EUR)']
strategy_RAMOM['CAD Future (USD)']
strategy_RAMOM['CHF Future (USD)']
strategy_RAMOM['S&P 500']
strategy_RAMOM['Nasdaq 100']
strategy_RAMOM['S&P Canada 60']
strategy_RAMOM['FTSE 100']
strategy_RAMOM['DAX']
strategy_RAMOM['SMI']
strategy_RAMOM['CAC - 40']
strategy_RAMOM['IBEX 35']
strategy_RAMOM['Euro Stoxx 50']
strategy_RAMOM['Nikkei 225']
strategy_RAMOM['Hang Seng Index']
strategy_RAMOM['US 2-year']
strategy_RAMOM['US 5-year']
strategy_RAMOM['US 10-year']
strategy_RAMOM['Long Gilt (10 year)']
strategy_RAMOM['Euro-Schatz-Futures (2 year)']
strategy_RAMOM['Euro-Bobl-Future (5 year)']
strategy_RAMOM['Euro-Bund-Future (10 year)']
strategy_RAMOM['Euro-Buxl-Future (30 year)']
strategy_RAMOM['Brent Crude Oil']
strategy_RAMOM['WTI Crude Oil']
strategy_RAMOM['Heating Oil']
strategy_RAMOM['Natural Gas']
strategy_RAMOM['Gold']
strategy_RAMOM['Silver']
strategy_RAMOM['Platinum']
strategy_RAMOM['Palladium']
strategy_RAMOM['Copper']
strategy_RAMOM['Nickel']
strategy_RAMOM['Zinc']
strategy_RAMOM['Lean hogs']
strategy_RAMOM['Live cattle']
strategy_RAMOM['Feeder cattle']
strategy_RAMOM['Wheat']
strategy_RAMOM['Corn']
strategy_RAMOM['Oat']
strategy_RAMOM['Soybeans']
strategy_RAMOM['Soybean oil']
strategy_RAMOM['Frozen orange juice']
strategy_RAMOM['Cocoa']
strategy_RAMOM['Coffee']
strategy_RAMOM['Sugar No.11']
strategy_RAMOM['Cotton No.2']
----------------------------------------
Nr. of Futures: 50


#start_date = '2000-01-01' 

# List to hold the data series
data_series_list_RAMOM = []

# Extract 'Cumulative_Log_Returns' and rename it uniquely
for name, df in strategy_RAMOM.items():
    if 'Cumulative_Log_Returns' in df.columns:
        # Filter the DataFrame to only include data from the start_date onwards
        filtered_df = df[df.index >= pd.to_datetime(start_date)]
        
        # Rename the series with its asset name and add to the list
        series = filtered_df['Cumulative_Log_Returns'].rename(name)
        data_series_list_RAMOM.append(series)

# Combine all the series into a single DataFrame
combined_df_RAMOM = pd.concat(data_series_list_RAMOM, axis=1, join='outer')

# Forward fill missing values
combined_df_RAMOM_filled = combined_df_RAMOM.ffill()

# Drop rows where any NaN values still exist (likely at the start)
combined_df_RAMOM_cleaned = combined_df_RAMOM_filled.dropna()


pd.set_option('display.max_columns', None)

combined_df_RAMOM_cleaned.head()


# Step 2: Calculate daily log returns from cumulative log returns
daily_log_returns_RAMOM = combined_df_RAMOM_cleaned.diff().fillna(0)

# Initialize a DataFrame to hold the adjusted weights
adjusted_weights = pd.DataFrame(index=daily_log_returns_RAMOM.index, columns=daily_log_returns_RAMOM.columns)

# Calculate weights dynamically each day using cumulative returns to check asset activity
for date, returns in combined_df_RAMOM_cleaned.iterrows():
    # Use the cumulative returns to determine active assets
    active_assets = returns[returns != 0].count()  # Count non-zero cumulative returns for the day
    if active_assets > 0:
        daily_weights = 1 / active_assets  # Equal weight divided by the number of active assets
    else:
        daily_weights = 0  # No active assets to invest in
    adjusted_weights.loc[date] = daily_weights

# Save the adjusted weights to an Excel file
adjusted_weights.to_excel('rebalanced_weights_equalweighted_RAMOM.xlsx')

# Step 3: Calculate portfolio returns using the dynamically adjusted weights
portfolio_RAMOM_log_returns = (daily_log_returns_RAMOM * adjusted_weights).sum(axis=1)
portfolio_RAMOM_simple_returns = np.exp(portfolio_RAMOM_log_returns) - 1
portfolio_RAMOM_cumulative_simple_returns = (1 + portfolio_RAMOM_simple_returns).cumprod() - 1

# Step 4: Plot the performance
trace = go.Scatter(x=combined_df_RAMOM_cleaned.index, y=portfolio_RAMOM_cumulative_simple_returns, mode='lines', name='Cumulative Simple Returns')
layout = go.Layout(title='Equal-weighted Portfolio RAMOM - Adjusted Over Time', xaxis=dict(title='Days'), yaxis=dict(title='Simple Return'))
fig = go.Figure(data=[trace], layout=layout)
fig.show()

<ipython-input-62-547a37fb0648>:18: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


for header, df in strategy_kalman.items():
    print(f"strategy_kalman['{header}']")
print('-' * 40)
print('Nr. of Futures: ' + str(len(strategy_kalman)))

strategy_kalman['USD Future']
strategy_kalman['EUR Future (USD)']
strategy_kalman['JPY Future (USD)']
strategy_kalman['GBP Future (USD)']
strategy_kalman['GBP Future (EUR)']
strategy_kalman['CAD Future (USD)']
strategy_kalman['CHF Future (USD)']
strategy_kalman['S&P 500']
strategy_kalman['Nasdaq 100']
strategy_kalman['S&P Canada 60']
strategy_kalman['FTSE 100']
strategy_kalman['DAX']
strategy_kalman['SMI']
strategy_kalman['CAC - 40']
strategy_kalman['IBEX 35']
strategy_kalman['Euro Stoxx 50']
strategy_kalman['Nikkei 225']
strategy_kalman['Hang Seng Index']
strategy_kalman['US 2-year']
strategy_kalman['US 5-year']
strategy_kalman['US 10-year']
strategy_kalman['Long Gilt (10 year)']
strategy_kalman['Euro-Schatz-Futures (2 year)']
strategy_kalman['Euro-Bobl-Future (5 year)']
strategy_kalman['Euro-Bund-Future (10 year)']
strategy_kalman['Euro-Buxl-Future (30 year)']
strategy_kalman['Brent Crude Oil']
strategy_kalman['WTI Crude Oil']
strategy_kalman['Heating Oil']
strategy_kalman['Natural Gas']
strategy_kalman['Gold']
strategy_kalman['Silver']
strategy_kalman['Platinum']
strategy_kalman['Palladium']
strategy_kalman['Copper']
strategy_kalman['Nickel']
strategy_kalman['Zinc']
strategy_kalman['Lean hogs']
strategy_kalman['Live cattle']
strategy_kalman['Feeder cattle']
strategy_kalman['Wheat']
strategy_kalman['Corn']
strategy_kalman['Oat']
strategy_kalman['Soybeans']
strategy_kalman['Soybean oil']
strategy_kalman['Frozen orange juice']
strategy_kalman['Cocoa']
strategy_kalman['Coffee']
strategy_kalman['Sugar No.11']
strategy_kalman['Cotton No.2']
----------------------------------------
Nr. of Futures: 50


#start_date = '2000-01-01' 

# List to hold the data series
data_series_list_kalman = []

# Extract 'Cumulative_Log_Returns' and rename it uniquely
for name, df in strategy_kalman.items():
    if 'Cumulative_Log_Returns' in df.columns:
        # Filter the DataFrame to only include data from the start_date onwards
        filtered_df = df[df.index >= pd.to_datetime(start_date)]
        
        # Rename the series with its asset name and add to the list
        series = filtered_df['Cumulative_Log_Returns'].rename(name)
        data_series_list_kalman.append(series)

# Combine all the series into a single DataFrame
combined_df_kalman = pd.concat(data_series_list_kalman, axis=1, join='outer')

# Forward fill missing values
combined_df_kalman_filled = combined_df_kalman.ffill()

# Drop rows where any NaN values still exist (likely at the start)
combined_df_kalman_cleaned = combined_df_kalman_filled.dropna()


pd.set_option('display.max_columns', None)

combined_df_kalman_cleaned.head()


# Step 2: Calculate daily log returns from cumulative log returns
daily_log_returns_kalman = combined_df_kalman_cleaned.diff().fillna(0)

# Initialize a DataFrame to hold the adjusted weights
adjusted_weights = pd.DataFrame(index=daily_log_returns_kalman.index, columns=daily_log_returns_kalman.columns)

# Calculate weights dynamically each day using cumulative returns to check asset activity
for date, returns in combined_df_kalman_cleaned.iterrows():
    # Use the cumulative returns to determine active assets
    active_assets = returns[returns != 0].count()  # Count non-zero cumulative returns for the day
    if active_assets > 0:
        daily_weights = 1 / active_assets  # Equal weight divided by the number of active assets
    else:
        daily_weights = 0  # No active assets to invest in
    adjusted_weights.loc[date] = daily_weights

# Save the adjusted weights to an Excel file
adjusted_weights.to_excel('rebalanced_weights_equalweighted_kalman.xlsx')

# Step 3: Calculate portfolio returns using the dynamically adjusted weights
portfolio_kalman_log_returns = (daily_log_returns_kalman * adjusted_weights).sum(axis=1)
portfolio_kalman_simple_returns = np.exp(portfolio_kalman_log_returns) - 1
portfolio_kalman_cumulative_simple_returns = (1 + portfolio_kalman_simple_returns).cumprod() - 1

# Step 4: Plot the performance
trace = go.Scatter(x=combined_df_kalman_cleaned.index, y=portfolio_kalman_cumulative_simple_returns, mode='lines', name='Cumulative Simple Returns')
layout = go.Layout(title='Equal-weighted Portfolio Kalman - Adjusted Over Time', xaxis=dict(title='Days'), yaxis=dict(title='Simple Return'))
fig = go.Figure(data=[trace], layout=layout)
fig.show()

<ipython-input-66-88a7dd3d8b5e>:18: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


window = 20


#strategy_start = '2000-01-01'
window_history = window + 10  # Define the window size as 15 days + 10 to have enough history
start_date = pd.to_datetime(strategy_start)  # Convert start_date to datetime object
adjusted_start_date = start_date - pd.DateOffset(days=window_history)  # Calculate adjusted start date

# Dictionary to store the filtered DataFrames
filtered_dfs = {}

# Loop through each header in new_headers
for header in new_headers:
    # Access the DataFrame for the current header
    df = strategy_SMA[header]

    # Check if the DataFrame has an index that includes the range we need
    if df.index.min() <= adjusted_start_date and df.index.max() >= start_date:
        # Filter the DataFrame to show data from adjusted_start_date to start_date
        filtered_df = df.loc[adjusted_start_date:start_date]
        
        # Store the filtered DataFrame in the dictionary
        filtered_dfs[header] = filtered_df


#initial_investment = 1000000
#strategy_start = '2000-01-01'

for header in new_headers:
    # Running total of log returns based on trade signals
    running_total = float(0)

    for date, row in filtered_dfs[header].iterrows():
        # Update running total based on trade signals
        if row['Signal_SMA_shifted'] == 1:  # Buy signal
            running_total += row['Log_Return']
        elif row['Signal_SMA_shifted'] == -1:  # Sell signal
            running_total -= row['Log_Return']
        
        # Calculate cumulative returns
        cumulative_log_return = np.exp(running_total) - 1 
        
        # Store the running total and the cumulative returns in the DataFrame
        filtered_dfs[header].loc[date, 'Cumulative_Log_Returns'] = running_total
        filtered_dfs[header].loc[date, 'Total_Log_Return'] = initial_investment * (1 + cumulative_log_return)

<ipython-input-69-d562b774e648>:19: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-69-d562b774e648>:20: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


# Assuming new_headers and filtered_dfs are already defined and populated
# List to hold the data series for total log returns
data_series_list_total_log_return = []

# Extract 'Total_Log_Return' from each filtered DataFrame and rename it uniquely
for header in new_headers:
    if 'Cumulative_Log_Returns' in filtered_dfs[header].columns:
        # Extract the series and rename it with its asset name
        series = filtered_dfs[header]['Cumulative_Log_Returns'].rename(header)
        data_series_list_total_log_return.append(series)

# Combine all the series into a single DataFrame
combined_df_total_log_return = pd.concat(data_series_list_total_log_return, axis=1, join='outer')

# Forward fill missing values, if necessary
combined_df_filled_total_log_return = combined_df_total_log_return.ffill()

# Drop rows where any NaN values still exist (likely at the start)
combined_df_cleaned_total_log_return = combined_df_filled_total_log_return.dropna()

# Concatenate the two DataFrames
combined_df_SMA_cleaned_vol = pd.concat([combined_df_cleaned_total_log_return, combined_df_SMA_cleaned])

# Check the concatenated result
combined_df_SMA_cleaned_vol.head(20)


window

20


# Function to calculate rebalanced weights based on 12-day volatility
def calculate_rebalanced_weights_volatility(df, window):
    # Calculate rolling standard deviation for each asset
    rolling_vol = df.diff().rolling(window=window).std().fillna(0)
    small_constant = 1e-8  # Small constant to avoid division by zero
    
    # Calculate inverse volatility, avoiding division by zero by adding small constant
    # Only consider non-zero volatilities in the weight calculation
    inverse_volatility = 1 / (rolling_vol + small_constant)
    inverse_volatility[rolling_vol == 0] = 0  # Set inverse volatility to zero where rolling volatility is zero
    
    # Sum of inverse volatilities (excluding those set to zero)
    sum_inverse_volatility = inverse_volatility.sum(axis=1)
    
    # Initialize daily weights DataFrame
    daily_weights = pd.DataFrame(index=df.index, columns=df.columns)
    # Assign weights daily based on non-zero inverse volatility
    for day in range(len(daily_weights)):
        daily_weights.iloc[day] = inverse_volatility.iloc[day] / sum_inverse_volatility.iloc[day]

    return daily_weights

# Ensure the DataFrame index is in datetime format
combined_df_SMA_cleaned_vol.index = pd.to_datetime(combined_df_SMA_cleaned_vol.index)

# Calculate daily rebalanced weights based on volatility using the full dataset
weights_SMA_vol = calculate_rebalanced_weights_volatility(combined_df_SMA_cleaned_vol, window)

# Starting calculation from the start date
#start_date = '2000-01-01'
# Filter the weights and returns dataframes from the start date
weights_SMA_vol_from_start = weights_SMA_vol[weights_SMA_vol.index >= start_date]
daily_log_returns_SMA_vol = combined_df_SMA_cleaned_vol.diff().fillna(0)[combined_df_SMA_cleaned_vol.index >= start_date]

# Calculate portfolio log returns using the new weights
portfolio_SMA_log_returns_vol = (daily_log_returns_SMA_vol * weights_SMA_vol_from_start).sum(axis=1)
portfolio_SMA_simple_returns_vol = np.exp(portfolio_SMA_log_returns_vol) - 1
portfolio_SMA_cumulative_simple_returns_vol = (1 + portfolio_SMA_simple_returns_vol).cumprod() - 1

# Plot the performance
trace = go.Scatter(x=weights_SMA_vol_from_start.index, y=portfolio_SMA_cumulative_simple_returns_vol, mode='lines', name='Cumulative Simple Returns with Daily Rebalancing')
data = [trace]
layout = go.Layout(title='Inverse Volatility Weighted Portfolio SMA with Daily Rebalancing - from 2000', xaxis=dict(title='Days'), yaxis=dict(title='Simple Return'))
fig = go.Figure(data=data, layout=layout)
fig.show()

# Save the weights to an Excel file
weights_SMA_vol_from_start.to_excel('rebalanced_weights_volatility_SMA.xlsx')

<ipython-input-72-0f0ec232555d>:48: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


#strategy_start = '2000-01-01'
window_history = window + 10  # Define the window size as 15 days + 10 to have enough history
start_date = pd.to_datetime(strategy_start)  # Convert start_date to datetime object
adjusted_start_date = start_date - pd.DateOffset(days=window_history)  # Calculate adjusted start date

# Dictionary to store the filtered DataFrames
filtered_dfs = {}

# Loop through each header in new_headers
for header in new_headers:
    # Access the DataFrame for the current header
    df = strategy_SMA_bands[header]

    # Check if the DataFrame has an index that includes the range we need
    if df.index.min() <= adjusted_start_date and df.index.max() >= start_date:
        # Filter the DataFrame to show data from adjusted_start_date to start_date
        filtered_df = df.loc[adjusted_start_date:start_date]
        
        # Store the filtered DataFrame in the dictionary
        filtered_dfs[header] = filtered_df


#initial_investment = 1000000
#strategy_start = '2000-01-01'

for header in new_headers:
    # Running total of log returns based on trade signals
    running_total = float(0)

    for date, row in filtered_dfs[header].iterrows():
        # Update running total based on trade signals
        if row['Signal_SMA_with_Band_shifted'] == 1:  # Buy signal
            running_total += row['Log_Return']
        elif row['Signal_SMA_with_Band_shifted'] == -1:  # Sell signal
            running_total -= row['Log_Return']
        
        # Calculate cumulative returns
        cumulative_log_return = np.exp(running_total) - 1 
        
        # Store the running total and the cumulative returns in the DataFrame
        filtered_dfs[header].loc[date, 'Cumulative_Log_Returns'] = running_total
        filtered_dfs[header].loc[date, 'Total_Log_Return'] = initial_investment * (1 + cumulative_log_return)

<ipython-input-74-b374e6ce1920>:19: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-74-b374e6ce1920>:20: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


# Assuming new_headers and filtered_dfs are already defined and populated
# List to hold the data series for total log returns
data_series_list_total_log_return = []

# Extract 'Total_Log_Return' from each filtered DataFrame and rename it uniquely
for header in new_headers:
    if 'Cumulative_Log_Returns' in filtered_dfs[header].columns:
        # Extract the series and rename it with its asset name
        series = filtered_dfs[header]['Cumulative_Log_Returns'].rename(header)
        data_series_list_total_log_return.append(series)

# Combine all the series into a single DataFrame
combined_df_total_log_return = pd.concat(data_series_list_total_log_return, axis=1, join='outer')

# Forward fill missing values, if necessary
combined_df_filled_total_log_return = combined_df_total_log_return.ffill()

# Drop rows where any NaN values still exist (likely at the start)
combined_df_cleaned_total_log_return = combined_df_filled_total_log_return.dropna()

# Concatenate the two DataFrames
combined_df_SMA_bands_cleaned_vol = pd.concat([combined_df_cleaned_total_log_return, combined_df_SMA_bands_cleaned])

# Check the concatenated result
combined_df_SMA_bands_cleaned_vol.head(20)


window

20


# Function to calculate rebalanced weights based on 12-day volatility
def calculate_rebalanced_weights_volatility(df, window):
    # Calculate rolling standard deviation for each asset
    rolling_vol = df.diff().rolling(window=window).std().fillna(0)
    small_constant = 1e-8  # Small constant to avoid division by zero
    
    # Calculate inverse volatility, avoiding division by zero by adding small constant
    # Only consider non-zero volatilities in the weight calculation
    inverse_volatility = 1 / (rolling_vol + small_constant)
    inverse_volatility[rolling_vol == 0] = 0  # Set inverse volatility to zero where rolling volatility is zero
    
    # Sum of inverse volatilities (excluding those set to zero)
    sum_inverse_volatility = inverse_volatility.sum(axis=1)
    
    # Initialize daily weights DataFrame
    daily_weights = pd.DataFrame(index=df.index, columns=df.columns)
    # Assign weights daily based on non-zero inverse volatility
    for day in range(len(daily_weights)):
        daily_weights.iloc[day] = inverse_volatility.iloc[day] / sum_inverse_volatility.iloc[day]

    return daily_weights

# Ensure the DataFrame index is in datetime format
combined_df_SMA_bands_cleaned_vol.index = pd.to_datetime(combined_df_SMA_bands_cleaned_vol.index)

# Calculate daily rebalanced weights based on volatility using the full dataset
weights_SMA_bands_vol = calculate_rebalanced_weights_volatility(combined_df_SMA_bands_cleaned_vol, window)

# Starting calculation from the start date
#start_date = '2000-01-01'
# Filter the weights and returns dataframes from the start date
weights_SMA_bands_vol_from_start = weights_SMA_bands_vol[weights_SMA_bands_vol.index >= start_date]
daily_log_returns_SMA_bands_vol = combined_df_SMA_bands_cleaned_vol.diff().fillna(0)[combined_df_SMA_bands_cleaned_vol.index >= start_date]

# Calculate portfolio log returns using the new weights
portfolio_SMA_bands_log_returns_vol = (daily_log_returns_SMA_bands_vol * weights_SMA_bands_vol_from_start).sum(axis=1)
portfolio_SMA_bands_simple_returns_vol = np.exp(portfolio_SMA_bands_log_returns_vol) - 1
portfolio_SMA_bands_cumulative_simple_returns_vol = (1 + portfolio_SMA_bands_simple_returns_vol).cumprod() - 1

# Plot the performance
trace = go.Scatter(x=weights_SMA_bands_vol_from_start.index, y=portfolio_SMA_bands_cumulative_simple_returns_vol, mode='lines', name='Cumulative Simple Returns with Daily Rebalancing')
data = [trace]
layout = go.Layout(title='Inverse Volatility Weighted Portfolio SMA with Bands with Daily Rebalancing - from 2000', xaxis=dict(title='Days'), yaxis=dict(title='Simple Return'))
fig = go.Figure(data=data, layout=layout)
fig.show()

# Save the weights to an Excel file
weights_SMA_bands_vol_from_start.to_excel('rebalanced_weights_volatility_SMA_bands.xlsx')

<ipython-input-77-1dd74f5067e8>:48: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


#strategy_start = '2000-01-01'
window_history = window + 10  # Define the window size as 15 days + 10 to have enough history
start_date = pd.to_datetime(strategy_start)  # Convert start_date to datetime object
adjusted_start_date = start_date - pd.DateOffset(days=window_history)  # Calculate adjusted start date

# Dictionary to store the filtered DataFrames
filtered_dfs = {}

# Loop through each header in new_headers
for header in new_headers:
    # Access the DataFrame for the current header
    df = strategy_TSMOM[header]

    # Check if the DataFrame has an index that includes the range we need
    if df.index.min() <= adjusted_start_date and df.index.max() >= start_date:
        # Filter the DataFrame to show data from adjusted_start_date to start_date
        filtered_df = df.loc[adjusted_start_date:start_date]
        
        # Store the filtered DataFrame in the dictionary
        filtered_dfs[header] = filtered_df


#initial_investment = 1000000
#strategy_start = '2000-01-01'

for header in new_headers:
    # Running total of log returns based on trade signals
    running_total = float(0)

    for date, row in filtered_dfs[header].iterrows():
        # Update running total based on trade signals
        if row[f'Signal_TSMOM_{TSMOM_window_month}_shifted'] == 1:  # Buy signal
            running_total += row['Log_Return']
        elif row[f'Signal_TSMOM_{TSMOM_window_month}_shifted'] == -1:  # Sell signal
            running_total -= row['Log_Return']
        
        # Calculate cumulative returns
        cumulative_log_return = np.exp(running_total) - 1 
        
        # Store the running total and the cumulative returns in the DataFrame
        filtered_dfs[header].loc[date, 'Cumulative_Log_Returns'] = running_total
        filtered_dfs[header].loc[date, 'Total_Log_Return'] = initial_investment * (1 + cumulative_log_return)

<ipython-input-79-4d91684a8daf>:19: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-79-4d91684a8daf>:20: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


# Assuming new_headers and filtered_dfs are already defined and populated
# List to hold the data series for total log returns
data_series_list_total_log_return = []

# Extract 'Total_Log_Return' from each filtered DataFrame and rename it uniquely
for header in new_headers:
    if 'Cumulative_Log_Returns' in filtered_dfs[header].columns:
        # Extract the series and rename it with its asset name
        series = filtered_dfs[header]['Cumulative_Log_Returns'].rename(header)
        data_series_list_total_log_return.append(series)

# Combine all the series into a single DataFrame
combined_df_total_log_return = pd.concat(data_series_list_total_log_return, axis=1, join='outer')

# Forward fill missing values, if necessary
combined_df_filled_total_log_return = combined_df_total_log_return.ffill()

# Drop rows where any NaN values still exist (likely at the start)
combined_df_cleaned_total_log_return = combined_df_filled_total_log_return.dropna()

# Concatenate the two DataFrames
combined_df_TSMOM_cleaned_vol = pd.concat([combined_df_cleaned_total_log_return, combined_df_TSMOM_cleaned])

# Check the concatenated result
combined_df_TSMOM_cleaned_vol.head(20)


window

20


# Function to calculate rebalanced weights based on 12-day volatility
def calculate_rebalanced_weights_volatility(df, window):
    # Calculate rolling standard deviation for each asset
    rolling_vol = df.diff().rolling(window=window).std().fillna(0)
    small_constant = 1e-8  # Small constant to avoid division by zero
    
    # Calculate inverse volatility, avoiding division by zero by adding small constant
    # Only consider non-zero volatilities in the weight calculation
    inverse_volatility = 1 / (rolling_vol + small_constant)
    inverse_volatility[rolling_vol == 0] = 0  # Set inverse volatility to zero where rolling volatility is zero
    
    # Sum of inverse volatilities (excluding those set to zero)
    sum_inverse_volatility = inverse_volatility.sum(axis=1)
    
    # Initialize daily weights DataFrame
    daily_weights = pd.DataFrame(index=df.index, columns=df.columns)
    # Assign weights daily based on non-zero inverse volatility
    for day in range(len(daily_weights)):
        daily_weights.iloc[day] = inverse_volatility.iloc[day] / sum_inverse_volatility.iloc[day]

    return daily_weights

# Ensure the DataFrame index is in datetime format
combined_df_TSMOM_cleaned_vol.index = pd.to_datetime(combined_df_TSMOM_cleaned_vol.index)

# Calculate daily rebalanced weights based on volatility using the full dataset
weights_TSMOM_vol = calculate_rebalanced_weights_volatility(combined_df_TSMOM_cleaned_vol, window)

# Starting calculation from the start date
#start_date = '2000-01-01'
# Filter the weights and returns dataframes from the start date
weights_TSMOM_vol_from_start = weights_TSMOM_vol[weights_TSMOM_vol.index >= start_date]
daily_log_returns_TSMOM_vol = combined_df_TSMOM_cleaned_vol.diff().fillna(0)[combined_df_TSMOM_cleaned_vol.index >= start_date]

# Calculate portfolio log returns using the new weights
portfolio_TSMOM_log_returns_vol = (daily_log_returns_TSMOM_vol * weights_TSMOM_vol_from_start).sum(axis=1)
portfolio_TSMOM_simple_returns_vol = np.exp(portfolio_TSMOM_log_returns_vol) - 1
portfolio_TSMOM_cumulative_simple_returns_vol = (1 + portfolio_TSMOM_simple_returns_vol).cumprod() - 1

# Plot the performance
trace = go.Scatter(x=weights_TSMOM_vol_from_start.index, y=portfolio_TSMOM_cumulative_simple_returns_vol, mode='lines', name='Cumulative Simple Returns with Daily Rebalancing')
data = [trace]
layout = go.Layout(title='Inverse Volatility Weighted Portfolio TSMOM with Daily Rebalancing - from 2000', xaxis=dict(title='Days'), yaxis=dict(title='Simple Return'))
fig = go.Figure(data=data, layout=layout)
fig.show()

# Save the weights to an Excel file
weights_TSMOM_vol_from_start.to_excel('rebalanced_weights_volatility_TSMOM.xlsx')

<ipython-input-82-7271abda57fc>:48: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


#strategy_start = '2000-01-01'
window_history = window + 10  # Define the window size as 15 days + 10 to have enough history
start_date = pd.to_datetime(strategy_start)  # Convert start_date to datetime object
adjusted_start_date = start_date - pd.DateOffset(days=window_history)  # Calculate adjusted start date

# Dictionary to store the filtered DataFrames
filtered_dfs = {}

# Loop through each header in new_headers
for header in new_headers:
    # Access the DataFrame for the current header
    df = strategy_RAMOM[header]

    # Check if the DataFrame has an index that includes the range we need
    if df.index.min() <= adjusted_start_date and df.index.max() >= start_date:
        # Filter the DataFrame to show data from adjusted_start_date to start_date
        filtered_df = df.loc[adjusted_start_date:start_date]
        
        # Store the filtered DataFrame in the dictionary
        filtered_dfs[header] = filtered_df


#initial_investment = 1000000
#strategy_start = '2000-01-01'

for header in new_headers:
    # Running total of log returns based on trade signals
    running_total = float(0)

    for date, row in filtered_dfs[header].iterrows():
        # Update running total based on trade signals
        if row[f'Signal_RAMOM_{RAMOM_window_month}_shifted'] == 1:  # Buy signal
            running_total += row['Log_Return']
        elif row[f'Signal_RAMOM_{RAMOM_window_month}_shifted'] == -1:  # Sell signal
            running_total -= row['Log_Return']
        
        # Calculate cumulative returns
        cumulative_log_return = np.exp(running_total) - 1 
        
        # Store the running total and the cumulative returns in the DataFrame
        filtered_dfs[header].loc[date, 'Cumulative_Log_Returns'] = running_total
        filtered_dfs[header].loc[date, 'Total_Log_Return'] = initial_investment * (1 + cumulative_log_return)

<ipython-input-84-098330b9dcc0>:19: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-84-098330b9dcc0>:20: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


# Assuming new_headers and filtered_dfs are already defined and populated
# List to hold the data series for total log returns
data_series_list_total_log_return = []

# Extract 'Total_Log_Return' from each filtered DataFrame and rename it uniquely
for header in new_headers:
    if 'Cumulative_Log_Returns' in filtered_dfs[header].columns:
        # Extract the series and rename it with its asset name
        series = filtered_dfs[header]['Cumulative_Log_Returns'].rename(header)
        data_series_list_total_log_return.append(series)

# Combine all the series into a single DataFrame
combined_df_total_log_return = pd.concat(data_series_list_total_log_return, axis=1, join='outer')

# Forward fill missing values, if necessary
combined_df_filled_total_log_return = combined_df_total_log_return.ffill()

# Drop rows where any NaN values still exist (likely at the start)
combined_df_cleaned_total_log_return = combined_df_filled_total_log_return.dropna()

# Concatenate the two DataFrames
combined_df_RAMOM_cleaned_vol = pd.concat([combined_df_cleaned_total_log_return, combined_df_RAMOM_cleaned])

# Check the concatenated result
combined_df_RAMOM_cleaned_vol.head(20)


window

20


# Function to calculate rebalanced weights based on 12-day volatility
def calculate_rebalanced_weights_volatility(df, window):
    # Calculate rolling standard deviation for each asset
    rolling_vol = df.diff().rolling(window=window).std().fillna(0)
    small_constant = 1e-8  # Small constant to avoid division by zero
    
    # Calculate inverse volatility, avoiding division by zero by adding small constant
    # Only consider non-zero volatilities in the weight calculation
    inverse_volatility = 1 / (rolling_vol + small_constant)
    inverse_volatility[rolling_vol == 0] = 0  # Set inverse volatility to zero where rolling volatility is zero
    
    # Sum of inverse volatilities (excluding those set to zero)
    sum_inverse_volatility = inverse_volatility.sum(axis=1)
    
    # Initialize daily weights DataFrame
    daily_weights = pd.DataFrame(index=df.index, columns=df.columns)
    # Assign weights daily based on non-zero inverse volatility
    for day in range(len(daily_weights)):
        daily_weights.iloc[day] = inverse_volatility.iloc[day] / sum_inverse_volatility.iloc[day]

    return daily_weights

# Ensure the DataFrame index is in datetime format
combined_df_RAMOM_cleaned_vol.index = pd.to_datetime(combined_df_RAMOM_cleaned_vol.index)

# Calculate daily rebalanced weights based on volatility using the full dataset
weights_RAMOM_vol = calculate_rebalanced_weights_volatility(combined_df_RAMOM_cleaned_vol, window)

# Starting calculation from the start date
#start_date = '2000-01-01'
# Filter the weights and returns dataframes from the start date
weights_RAMOM_vol_from_start = weights_RAMOM_vol[weights_RAMOM_vol.index >= start_date]
daily_log_returns_RAMOM_vol = combined_df_RAMOM_cleaned_vol.diff().fillna(0)[combined_df_RAMOM_cleaned_vol.index >= start_date]

# Calculate portfolio log returns using the new weights
portfolio_RAMOM_log_returns_vol = (daily_log_returns_RAMOM_vol * weights_RAMOM_vol_from_start).sum(axis=1)
portfolio_RAMOM_simple_returns_vol = np.exp(portfolio_RAMOM_log_returns_vol) - 1
portfolio_RAMOM_cumulative_simple_returns_vol = (1 + portfolio_RAMOM_simple_returns_vol).cumprod() - 1

# Plot the performance
trace = go.Scatter(x=weights_RAMOM_vol_from_start.index, y=portfolio_RAMOM_cumulative_simple_returns_vol, mode='lines', name='Cumulative Simple Returns with Daily Rebalancing')
data = [trace]
layout = go.Layout(title='Inverse Volatility Weighted Portfolio RAMOM with Daily Rebalancing - from 2000', xaxis=dict(title='Days'), yaxis=dict(title='Simple Return'))
fig = go.Figure(data=data, layout=layout)
fig.show()

# Save the weights to an Excel file
weights_RAMOM_vol_from_start.to_excel('rebalanced_weights_volatility_RAMOM.xlsx')

<ipython-input-87-fbd277bcf19a>:48: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


#strategy_start = '2000-01-01'
window_history = window + 10  # Define the window size as 15 days + 10 to have enough history
start_date = pd.to_datetime(strategy_start)  # Convert start_date to datetime object
adjusted_start_date = start_date - pd.DateOffset(days=window_history)  # Calculate adjusted start date

# Dictionary to store the filtered DataFrames
filtered_dfs = {}

# Loop through each header in new_headers
for header in new_headers:
    # Access the DataFrame for the current header
    df = strategy_kalman[header]

    # Check if the DataFrame has an index that includes the range we need
    if df.index.min() <= adjusted_start_date and df.index.max() >= start_date:
        # Filter the DataFrame to show data from adjusted_start_date to start_date
        filtered_df = df.loc[adjusted_start_date:start_date]
        
        # Store the filtered DataFrame in the dictionary
        filtered_dfs[header] = filtered_df


#initial_investment = 1000000
#strategy_start = '2000-01-01'

for header in new_headers:
    # Running total of log returns based on trade signals
    running_total = float(0)

    for date, row in filtered_dfs[header].iterrows():
        # Update running total based on trade signals
        if row['Signal_Kalman_Filter_shifted'] == 1:  # Buy signal
            running_total += row['Log_Return']
        elif row['Signal_Kalman_Filter_shifted'] == -1:  # Sell signal
            running_total -= row['Log_Return']
        
        # Calculate cumulative returns
        cumulative_log_return = np.exp(running_total) - 1 
        
        # Store the running total and the cumulative returns in the DataFrame
        filtered_dfs[header].loc[date, 'Cumulative_Log_Returns'] = running_total
        filtered_dfs[header].loc[date, 'Total_Log_Return'] = initial_investment * (1 + cumulative_log_return)

<ipython-input-89-0dca73fa82fc>:19: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-89-0dca73fa82fc>:20: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


# Assuming new_headers and filtered_dfs are already defined and populated
# List to hold the data series for total log returns
data_series_list_total_log_return = []

# Extract 'Total_Log_Return' from each filtered DataFrame and rename it uniquely
for header in new_headers:
    if 'Cumulative_Log_Returns' in filtered_dfs[header].columns:
        # Extract the series and rename it with its asset name
        series = filtered_dfs[header]['Cumulative_Log_Returns'].rename(header)
        data_series_list_total_log_return.append(series)

# Combine all the series into a single DataFrame
combined_df_total_log_return = pd.concat(data_series_list_total_log_return, axis=1, join='outer')

# Forward fill missing values, if necessary
combined_df_filled_total_log_return = combined_df_total_log_return.ffill()

# Drop rows where any NaN values still exist (likely at the start)
combined_df_cleaned_total_log_return = combined_df_filled_total_log_return.dropna()

# Concatenate the two DataFrames
combined_df_kalman_cleaned_vol = pd.concat([combined_df_cleaned_total_log_return, combined_df_kalman_cleaned])

# Check the concatenated result
combined_df_kalman_cleaned_vol.head(20)


window

20


# Function to calculate rebalanced weights based on 12-day volatility
def calculate_rebalanced_weights_volatility(df, window):
    # Calculate rolling standard deviation for each asset
    rolling_vol = df.diff().rolling(window=window).std().fillna(0)
    small_constant = 1e-8  # Small constant to avoid division by zero
    
    # Calculate inverse volatility, avoiding division by zero by adding small constant
    # Only consider non-zero volatilities in the weight calculation
    inverse_volatility = 1 / (rolling_vol + small_constant)
    inverse_volatility[rolling_vol == 0] = 0  # Set inverse volatility to zero where rolling volatility is zero
    
    # Sum of inverse volatilities (excluding those set to zero)
    sum_inverse_volatility = inverse_volatility.sum(axis=1)
    
    # Initialize daily weights DataFrame
    daily_weights = pd.DataFrame(index=df.index, columns=df.columns)
    # Assign weights daily based on non-zero inverse volatility
    for day in range(len(daily_weights)):
        daily_weights.iloc[day] = inverse_volatility.iloc[day] / sum_inverse_volatility.iloc[day]

    return daily_weights

# Ensure the DataFrame index is in datetime format
combined_df_kalman_cleaned_vol.index = pd.to_datetime(combined_df_kalman_cleaned_vol.index)

# Calculate daily rebalanced weights based on volatility using the full dataset
weights_kalman_vol = calculate_rebalanced_weights_volatility(combined_df_kalman_cleaned_vol, window)

# Starting calculation from the start date
#start_date = '2000-01-01'
# Filter the weights and returns dataframes from the start date
weights_kalman_vol_from_start = weights_kalman_vol[weights_kalman_vol.index >= start_date]
daily_log_returns_kalman_vol = combined_df_kalman_cleaned_vol.diff().fillna(0)[combined_df_kalman_cleaned_vol.index >= start_date]

# Calculate portfolio log returns using the new weights
portfolio_kalman_log_returns_vol = (daily_log_returns_kalman_vol * weights_kalman_vol_from_start).sum(axis=1)
portfolio_kalman_simple_returns_vol = np.exp(portfolio_kalman_log_returns_vol) - 1
portfolio_kalman_cumulative_simple_returns_vol = (1 + portfolio_kalman_simple_returns_vol).cumprod() - 1

# Plot the performance
trace = go.Scatter(x=weights_kalman_vol_from_start.index, y=portfolio_kalman_cumulative_simple_returns_vol, mode='lines', name='Cumulative Simple Returns with Daily Rebalancing')
data = [trace]
layout = go.Layout(title='Inverse Volatility Weighted Portfolio Kalman with Daily Rebalancing - from 2000', xaxis=dict(title='Days'), yaxis=dict(title='Simple Return'))
fig = go.Figure(data=data, layout=layout)
fig.show()

# Save the weights to an Excel file
weights_kalman_vol_from_start.to_excel('rebalanced_weights_volatility_kalman.xlsx')

<ipython-input-92-9d68be3fe576>:48: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


SPY = qs.utils.download_returns('SPY')
SPY_df = pd.DataFrame(SPY)
SPY_df.index = pd.to_datetime(SPY_df.index)
SPY_df_filterd = SPY_df['2000-01-05':'2023-12-31']
SPY = SPY_df_filterd['Close']
SPY['Cumulative_simple_return'] = (1 + SPY).cumprod() - 1

[*********************100%***********************]  1 of 1 completed

<ipython-input-93-99472245db3f>:6: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


# Creating the trace for the current feature
trace = go.Scatter(x=SPY.index, y=SPY['Cumulative_simple_return'], mode='lines', name='Simple return')

# Creating the data list with the current trace
data = [trace]

# Creating the layout for the current plot, dynamically setting the title to the current feature
layout = go.Layout(title='SPY - over Time', yaxis=dict(title='Simple return'))

# Creating the figure with the current data and layout
fig = go.Figure(data=data, layout=layout)

# Displaying the figure
fig.show()


BarclayHedgeCTA = pd.read_excel('BarclayHedge CTA Index.xlsx', index_col = 'Date', parse_dates = True)
BarclayHedgeCTA['Simple_returns'] = BarclayHedgeCTA['Price'] / BarclayHedgeCTA['Price'].shift(1) - 1
BarclayHedgeCTA['Log_Returns'] = np.log(BarclayHedgeCTA['Price'] / BarclayHedgeCTA['Price'].shift(1))

BarclayHedgeCTA_filtered = BarclayHedgeCTA.loc[strategy_start:]
BarclayHedgeCTA_filtered['Cumulative_Log_Returns'] = BarclayHedgeCTA_filtered['Log_Returns'].cumsum()
BarclayHedgeCTA_filtered['Cumulative_simple_return'] = (1 + BarclayHedgeCTA_filtered['Simple_returns']).cumprod() - 1

BarclayHedgeCTA_filtered.head(10)

<ipython-input-95-2ae0cf60a41d>:6: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

<ipython-input-95-2ae0cf60a41d>:7: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


what_to_plot = 'BarclayHedgeCTA'

# Creating the trace for the current feature
trace = go.Scatter(x=BarclayHedgeCTA.index, y=BarclayHedgeCTA_filtered['Cumulative_simple_return'], mode='lines', name='Simple return Price')

# Creating the data list with the current trace
data = [trace]

# Creating the layout for the current plot, dynamically setting the title to the current feature
layout = go.Layout(title='BarclayHedge CTA Index - over Time', xaxis=dict(title='Days'), yaxis=dict(title='Simple return'))

# Creating the figure with the current data and layout
fig = go.Figure(data=data, layout=layout)

# Displaying the figure
fig.show()


# Line SPY
trace_SPY = go.Scatter(
    x=SPY.index, 
    y=SPY['Cumulative_simple_return'], 
    mode='lines', 
    name='SPY'
)

# Line BarclayHedge
trace_BarclayHedge = go.Scatter(
    x=BarclayHedgeCTA_filtered.index, 
    y=BarclayHedgeCTA_filtered['Cumulative_simple_return'], 
    mode='lines', 
    name='BarclayHedgeCTA Index'
)

# Line SMA
trace_SMA = go.Scatter(
    x=combined_df_SMA_cleaned.index, 
    y=portfolio_SMA_cumulative_simple_returns, 
    mode='lines', 
    name='SMA EW'
)

# Line SMA with Bands
trace_SMA_bands = go.Scatter(
    x=combined_df_SMA_bands_cleaned.index, 
    y=portfolio_SMA_bands_cumulative_simple_returns, 
    mode='lines', 
    name='SMA with Bands EW'
)

# Line TSMOM
trace_TSMOM = go.Scatter(
    x=combined_df_TSMOM_cleaned.index, 
    y=portfolio_TSMOM_cumulative_simple_returns, 
    mode='lines', 
    name='TSMOM EW'
)

# Line RAMOM
trace_RAMOM = go.Scatter(
    x=combined_df_RAMOM_cleaned.index, 
    y=portfolio_RAMOM_cumulative_simple_returns, 
    mode='lines', 
    name='RAMOM EW'
)

# Line Kalman
trace_kalman = go.Scatter(
    x=combined_df_kalman_cleaned.index, 
    y=portfolio_kalman_cumulative_simple_returns, 
    mode='lines', 
    name='Kalman Filter EW'
)

# Combine all traces
data = [trace_SPY, trace_BarclayHedge, trace_SMA, trace_SMA_bands, trace_TSMOM, trace_RAMOM, trace_kalman]

# Define layout
layout = go.Layout(
    title={
        'text': 'All equal weighted portfolios over time',
        'y': 0.9,
        'x': 0.5,
        'xanchor': 'center',
        'yanchor': 'top',
        'font': dict(size=18, color='black', family='Arial'),
    },
    plot_bgcolor='white',
    paper_bgcolor='white',
    xaxis=dict(
        showgrid=True,
        gridcolor='lightgrey',
        tickangle=-45  # Rotate the x-axis labels by 45 degrees
    ),
    yaxis=dict(
        title='Simple Return',
        showgrid=True,
        gridcolor='lightgrey'
    )
)

# Create figure and add data and layout
fig = go.Figure(data=data, layout=layout)

# Show the figure
fig.show()


# Simple Return Data for the EW:

return_data = [portfolio_SMA_simple_returns,
               portfolio_SMA_bands_simple_returns,
               portfolio_TSMOM_simple_returns,
               portfolio_RAMOM_simple_returns,
               portfolio_kalman_simple_returns
               ]


# Making it possible to use it as Benchmark
BarclayHedgeCTA = BarclayHedgeCTA_filtered['Simple_returns']
BarclayHedgeCTA_df = pd.DataFrame(BarclayHedgeCTA)
BarclayHedgeCTA_df.columns = ['BarclayHedgeCTA']


qs.plots.snapshot(BarclayHedgeCTA, title='BarclayHedgeCTA', show=True)


qs.plots.snapshot(portfolio_SMA_simple_returns, title='SMA EW', show=True)
qs.reports.html(portfolio_SMA_simple_returns, BarclayHedgeCTA_df['BarclayHedgeCTA'], title='SMA Strategy Tearsheet EW', download_filename='SMA Strategy Tearsheet EW.html')

C:\Users\Raphael\anaconda3\lib\site-packages\seaborn\categorical.py:82: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.


qs.plots.snapshot(portfolio_SMA_bands_simple_returns, title='SMA with Bands EW', show=True)
qs.reports.html(portfolio_SMA_bands_simple_returns, BarclayHedgeCTA_df['BarclayHedgeCTA'], title='SMA with Bands Strategy Tearsheet EW', download_filename='SMA with Bands Strategy Tearsheet EW.html')

C:\Users\Raphael\anaconda3\lib\site-packages\seaborn\categorical.py:82: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.


qs.plots.snapshot(portfolio_TSMOM_simple_returns, title='TSMOM EW', show=True)
qs.reports.html(portfolio_TSMOM_simple_returns, BarclayHedgeCTA_df['BarclayHedgeCTA'], title='TSMOM Strategy Tearsheet EW', download_filename='TSMOM Strategy Tearsheet EW.html')

C:\Users\Raphael\anaconda3\lib\site-packages\seaborn\categorical.py:82: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.


qs.plots.snapshot(portfolio_RAMOM_simple_returns, title='RAMOM EW', show=True)
qs.reports.html(portfolio_RAMOM_simple_returns, BarclayHedgeCTA_df['BarclayHedgeCTA'], title='RAMOM Strategy Tearsheet EW', download_filename='RAMOM Strategy Tearsheet EW.html')

C:\Users\Raphael\anaconda3\lib\site-packages\seaborn\categorical.py:82: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.


qs.plots.snapshot(portfolio_kalman_simple_returns, title='Kalman EW', show=True)
qs.reports.html(portfolio_kalman_simple_returns, BarclayHedgeCTA_df['BarclayHedgeCTA'], title='Kalman Strategy Tearsheet EW', download_filename='Kalman Strategy Tearsheet EW.html')

C:\Users\Raphael\anaconda3\lib\site-packages\seaborn\categorical.py:82: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.


# Line SPY
trace_SPY = go.Scatter(
    x=SPY.index, 
    y=SPY['Cumulative_simple_return'], 
    mode='lines', 
    name='SPY'
)

# Line BarclayHedge
trace_BarclayHedge = go.Scatter(
    x=BarclayHedgeCTA_filtered.index, 
    y=BarclayHedgeCTA_filtered['Cumulative_simple_return'], 
    mode='lines', 
    name='BarclayHedgeCTA Index'
)

# Line SMA
trace_SMA = go.Scatter(
    x=combined_df_SMA_cleaned.index, 
    y=portfolio_SMA_cumulative_simple_returns_vol, 
    mode='lines', 
    name='SMA VW'
)

# Line SMA with Bands
trace_SMA_bands = go.Scatter(
    x=combined_df_SMA_bands_cleaned.index, 
    y=portfolio_SMA_bands_cumulative_simple_returns_vol, 
    mode='lines', 
    name='SMA with Bands VW'
)

# Line TSMOM
trace_TSMOM = go.Scatter(
    x=combined_df_TSMOM_cleaned.index, 
    y=portfolio_TSMOM_cumulative_simple_returns_vol, 
    mode='lines', 
    name='TSMOM VW'
)

# Line RAMOM
trace_RAMOM = go.Scatter(
    x=combined_df_RAMOM_cleaned.index, 
    y=portfolio_RAMOM_cumulative_simple_returns_vol, 
    mode='lines', 
    name='RAMOM VW'
)

# Line Kalman
trace_kalman = go.Scatter(
    x=combined_df_kalman_cleaned.index, 
    y=portfolio_kalman_cumulative_simple_returns_vol, 
    mode='lines', 
    name='Kalman Filter VW'
)

# Combine all traces
data = [trace_SPY, trace_BarclayHedge, trace_SMA, trace_SMA_bands, trace_TSMOM, trace_RAMOM, trace_kalman]

# Define layout
layout = go.Layout(
    title={
        'text': 'All volatility weighted portfolios over time',
        'y': 0.9,
        'x': 0.5,
        'xanchor': 'center',
        'yanchor': 'top',
        'font': dict(size=18, color='black', family='Arial'),
    },
    plot_bgcolor='white',
    paper_bgcolor='white',
    xaxis=dict(
        showgrid=True,
        gridcolor='lightgrey',
        tickangle=-45  # Rotate the x-axis labels by 45 degrees
    ),
    yaxis=dict(
        title='Simple Return',
        showgrid=True,
        gridcolor='lightgrey'
    )
)

# Create figure and add data and layout
fig = go.Figure(data=data, layout=layout)

# Show the figure
fig.show()


# Simple Return Data for VW
return_data_vol = [portfolio_SMA_simple_returns_vol,
                   portfolio_SMA_bands_simple_returns_vol,
                   portfolio_TSMOM_simple_returns_vol,
                   portfolio_RAMOM_simple_returns_vol,
                   portfolio_kalman_simple_returns_vol
                  ]


qs.plots.snapshot(BarclayHedgeCTA, title='BarclayHedgeCTA', show=True)


qs.plots.snapshot(portfolio_SMA_simple_returns_vol, title='SMA VW', show=True)
qs.reports.html(portfolio_SMA_simple_returns_vol, BarclayHedgeCTA_df['BarclayHedgeCTA'], title='SMA Strategy Tearsheet VW', download_filename='SMA Strategy Tearsheet VW.html')

C:\Users\Raphael\anaconda3\lib\site-packages\seaborn\categorical.py:82: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.


qs.plots.snapshot(portfolio_SMA_bands_simple_returns_vol, title='SMA with Bands VW', show=True)
qs.reports.html(portfolio_SMA_bands_simple_returns_vol, BarclayHedgeCTA_df['BarclayHedgeCTA'], title='SMA with Bands Strategy Tearsheet VW', download_filename='SMA with Bands Strategy Tearsheet VW.html')

C:\Users\Raphael\anaconda3\lib\site-packages\seaborn\categorical.py:82: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.


qs.plots.snapshot(portfolio_TSMOM_simple_returns_vol, title='TSMOM VW', show=True)
qs.reports.html(portfolio_TSMOM_simple_returns_vol, BarclayHedgeCTA_df['BarclayHedgeCTA'], title='TSMOM Strategy Tearsheet VW', download_filename='TSMOM Strategy Tearsheet VW.html')

C:\Users\Raphael\anaconda3\lib\site-packages\seaborn\categorical.py:82: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.


qs.plots.snapshot(portfolio_RAMOM_simple_returns_vol, title='RAMOM VW', show=True)
qs.reports.html(portfolio_RAMOM_simple_returns_vol, BarclayHedgeCTA_df['BarclayHedgeCTA'], title='RAMOM Strategy Tearsheet VW', download_filename='RAMOM Strategy Tearsheet VW.html')

C:\Users\Raphael\anaconda3\lib\site-packages\seaborn\categorical.py:82: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.


qs.plots.snapshot(portfolio_kalman_simple_returns_vol, title='Kalman VW', show=True)
qs.reports.html(portfolio_kalman_simple_returns_vol, BarclayHedgeCTA_df['BarclayHedgeCTA'], title='Kalman Strategy Tearsheet VW', download_filename='Kalman Strategy Tearsheet VW.html')

C:\Users\Raphael\anaconda3\lib\site-packages\seaborn\categorical.py:82: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.


SPY = qs.utils.download_returns('SPY')
SPY_df = pd.DataFrame(SPY)
SPY_df.index = pd.to_datetime(SPY_df.index)
SPY_df_filterd = SPY_df['2000-01-05':'2023-12-31']
SPY = SPY_df_filterd['Close']

[*********************100%***********************]  1 of 1 completed


# Assuming each strategy variable is already defined and holds return data
strategies = {
    "SPY": SPY,
    "BarclayHedgeCTA": BarclayHedgeCTA,
    "SMA EW": portfolio_SMA_simple_returns,
    "SMA with Bands EW": portfolio_SMA_bands_simple_returns,
    "TSMOM EW": portfolio_TSMOM_simple_returns,
    "RAMOM EW": portfolio_RAMOM_simple_returns,
    "Kalman EW": portfolio_kalman_simple_returns,
    "SMA VW": portfolio_SMA_simple_returns_vol,
    "SMA with Bands VW": portfolio_SMA_bands_simple_returns_vol,
    "TSMOM VW": portfolio_TSMOM_simple_returns_vol,
    "RAMOM VW": portfolio_RAMOM_simple_returns_vol,
    "Kalman VW": portfolio_kalman_simple_returns_vol
}

# Create a DataFrame to store the results
results = pd.DataFrame()

# Calculate metrics for each strategy
for name, data in strategies.items():
    if name == "BarclayHedgeCTA":
        # Special calculations for BarclayHedgeCTA
        results.loc[name, 'Total Return'] = qs.stats.comp(data)
        results.loc[name, 'Annualized Volatility'] = qs.stats.volatility(data, periods=12)
        results.loc[name, 'Sharpe Ratio'] = qs.stats.sharpe(data, periods=12)
        results.loc[name, 'Max Drawdown'] = qs.stats.max_drawdown(data)
        results.loc[name, 'Sortino Ratio'] = qs.stats.sortino(data, periods=12)
        results.loc[name, 'Beta SPY'] = qs.stats.greeks(data, benchmark=SPY)['beta']
    else:
        # General calculations for all other strategies
        results.loc[name, 'Total Return'] = qs.stats.comp(data)
        results.loc[name, 'Annualized Volatility'] = qs.stats.volatility(data)
        results.loc[name, 'Sharpe Ratio'] = qs.stats.sharpe(data)
        results.loc[name, 'Max Drawdown'] = qs.stats.max_drawdown(data)
        results.loc[name, 'Sortino Ratio'] = qs.stats.sortino(data)
        if name != 'SPY':  # Assuming you don't want to calculate beta for SPY against itself
            greek = qs.stats.greeks(data, benchmark=SPY)
            results.loc[name, 'Beta SPY'] = greek['beta']
        if name != 'BarclayHedgeCTA':  # Assuming you don't want to calculate beta for SPY against itself
            greek = qs.stats.greeks(data, benchmark=BarclayHedgeCTA)
            results.loc[name, 'Beta CTA'] = greek['beta']

# Save results to Excel
results.to_excel('Results_Overall.xlsx')

# Display the results DataFrame
results

<ipython-input-114-512920e91555>:45: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


# Assuming each strategy variable is already defined and holds return data
strategies = {
    "SPY": SPY,
    "BarclayHedgeCTA": BarclayHedgeCTA,
    "SMA EW": portfolio_SMA_simple_returns,
    "SMA with Bands EW": portfolio_SMA_bands_simple_returns,
    "TSMOM EW": portfolio_TSMOM_simple_returns,
    "RAMOM EW": portfolio_RAMOM_simple_returns,
    "Kalman EW": portfolio_kalman_simple_returns,
    "SMA VW": portfolio_SMA_simple_returns_vol,
    "SMA with Bands VW": portfolio_SMA_bands_simple_returns_vol,
    "TSMOM VW": portfolio_TSMOM_simple_returns_vol,
    "RAMOM VW": portfolio_RAMOM_simple_returns_vol,
    "Kalman VW": portfolio_kalman_simple_returns_vol
}

# Create a DataFrame to store the results
results_2000_recession = pd.DataFrame()

# Define subperiod
start_date = '2001-03-01'
end_date = '2001-11-30'

# Calculate metrics for each strategy during the subperiod
for name, data in strategies.items():
    # Filter data for the subperiod
    subperiod_data_2000_recession = data[start_date:end_date]

    if name == "BarclayHedgeCTA":
        # Special calculations for BarclayHedgeCTA
        results_2000_recession.loc[name, 'Total Return'] = qs.stats.comp(subperiod_data_2000_recession)
        results_2000_recession.loc[name, 'Annualized Volatility'] = qs.stats.volatility(subperiod_data_2000_recession, periods=12)
        results_2000_recession.loc[name, 'Sharpe Ratio'] = qs.stats.sharpe(subperiod_data_2000_recession, periods=12)
        results_2000_recession.loc[name, 'Max Drawdown'] = qs.stats.max_drawdown(subperiod_data_2000_recession)
        results_2000_recession.loc[name, 'Sortino Ratio'] = qs.stats.sortino(subperiod_data_2000_recession, periods=12)
        results_2000_recession.loc[name, 'Beta SPY'] = qs.stats.greeks(subperiod_data_2000_recession, benchmark=SPY[start_date:end_date])['beta']
    else:
        # General calculations for all other strategies
        results_2000_recession.loc[name, 'Total Return'] = qs.stats.comp(subperiod_data_2000_recession)
        results_2000_recession.loc[name, 'Annualized Volatility'] = qs.stats.volatility(subperiod_data_2000_recession)
        results_2000_recession.loc[name, 'Sharpe Ratio'] = qs.stats.sharpe(subperiod_data_2000_recession)
        results_2000_recession.loc[name, 'Max Drawdown'] = qs.stats.max_drawdown(subperiod_data_2000_recession)
        results_2000_recession.loc[name, 'Sortino Ratio'] = qs.stats.sortino(subperiod_data_2000_recession)
        if name != 'SPY':  # Assuming you don't want to calculate beta for SPY against itself
            greek = qs.stats.greeks(subperiod_data_2000_recession, benchmark=SPY[start_date:end_date])
            results_2000_recession.loc[name, 'Beta SPY'] = greek['beta']
        if name != 'BarclayHedgeCTA':  # Assuming you don't want to calculate beta for SPY against itself
            greek = qs.stats.greeks(subperiod_data_2000_recession, benchmark=BarclayHedgeCTA[start_date:end_date])
            results_2000_recession.loc[name, 'Beta CTA'] = greek['beta']

# Save results to Excel (optional)
results_2000_recession.to_excel('Results_2000_recession.xlsx')

# Display the results DataFrame
results_2000_recession

<ipython-input-115-261f959634d1>:52: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


# Assuming each strategy variable is already defined and holds return data
strategies = {
    "SPY": SPY,
    "BarclayHedgeCTA": BarclayHedgeCTA,
    "SMA EW": portfolio_SMA_simple_returns,
    "SMA with Bands EW": portfolio_SMA_bands_simple_returns,
    "TSMOM EW": portfolio_TSMOM_simple_returns,
    "RAMOM EW": portfolio_RAMOM_simple_returns,
    "Kalman EW": portfolio_kalman_simple_returns,
    "SMA VW": portfolio_SMA_simple_returns_vol,
    "SMA with Bands VW": portfolio_SMA_bands_simple_returns_vol,
    "TSMOM VW": portfolio_TSMOM_simple_returns_vol,
    "RAMOM VW": portfolio_RAMOM_simple_returns_vol,
    "Kalman VW": portfolio_kalman_simple_returns_vol
}

# Create a DataFrame to store the results
results_great_recession = pd.DataFrame()

# Define subperiod
start_date = '2007-12-01'
end_date = '2009-06-30'

# Calculate metrics for each strategy during the subperiod
for name, data in strategies.items():
    # Filter data for the subperiod
    subperiod_data_great_recession = data[start_date:end_date]

    if name == "BarclayHedgeCTA":
        # Special calculations for BarclayHedgeCTA
        results_great_recession.loc[name, 'Total Return'] = qs.stats.comp(subperiod_data_great_recession)
        results_great_recession.loc[name, 'Annualized Volatility'] = qs.stats.volatility(subperiod_data_great_recession, periods=12)
        results_great_recession.loc[name, 'Sharpe Ratio'] = qs.stats.sharpe(subperiod_data_great_recession, periods=12)
        results_great_recession.loc[name, 'Max Drawdown'] = qs.stats.max_drawdown(subperiod_data_great_recession)
        results_great_recession.loc[name, 'Sortino Ratio'] = qs.stats.sortino(subperiod_data_great_recession, periods=12)
        results_great_recession.loc[name, 'Beta SPY'] = qs.stats.greeks(subperiod_data_great_recession, benchmark=SPY[start_date:end_date])['beta']
    else:
        # General calculations for all other strategies
        results_great_recession.loc[name, 'Total Return'] = qs.stats.comp(subperiod_data_great_recession)
        results_great_recession.loc[name, 'Annualized Volatility'] = qs.stats.volatility(subperiod_data_great_recession)
        results_great_recession.loc[name, 'Sharpe Ratio'] = qs.stats.sharpe(subperiod_data_great_recession)
        results_great_recession.loc[name, 'Max Drawdown'] = qs.stats.max_drawdown(subperiod_data_great_recession)
        results_great_recession.loc[name, 'Sortino Ratio'] = qs.stats.sortino(subperiod_data_great_recession)
        if name != 'SPY':  # Assuming you don't want to calculate beta for SPY against itself
            greek = qs.stats.greeks(subperiod_data_great_recession, benchmark=SPY[start_date:end_date])
            results_great_recession.loc[name, 'Beta SPY'] = greek['beta']
        if name != 'BarclayHedgeCTA':  # Assuming you don't want to calculate beta for SPY against itself
            greek = qs.stats.greeks(subperiod_data_great_recession, benchmark=BarclayHedgeCTA[start_date:end_date])
            results_great_recession.loc[name, 'Beta CTA'] = greek['beta']

# Save results to Excel (optional)
results_great_recession.to_excel('Results_great_recession.xlsx')

# Display the results DataFrame
results_great_recession

<ipython-input-116-7da8a2c97c1c>:52: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).


# Assuming each strategy variable is already defined and holds return data
strategies = {
    "SPY": SPY,
    "BarclayHedgeCTA": BarclayHedgeCTA,
    "SMA EW": portfolio_SMA_simple_returns,
    "SMA with Bands EW": portfolio_SMA_bands_simple_returns,
    "TSMOM EW": portfolio_TSMOM_simple_returns,
    "RAMOM EW": portfolio_RAMOM_simple_returns,
    "Kalman EW": portfolio_kalman_simple_returns,
    "SMA VW": portfolio_SMA_simple_returns_vol,
    "SMA with Bands VW": portfolio_SMA_bands_simple_returns_vol,
    "TSMOM VW": portfolio_TSMOM_simple_returns_vol,
    "RAMOM VW": portfolio_RAMOM_simple_returns_vol,
    "Kalman VW": portfolio_kalman_simple_returns_vol
}

# Create a DataFrame to store the results
results_covid = pd.DataFrame()

# Define subperiod
start_date = '2020-02-01'
end_date = '2020-04-30'

# Calculate metrics for each strategy during the subperiod
for name, data in strategies.items():
    # Filter data for the subperiod
    subperiod_data_covid = data[start_date:end_date]

    if name == "BarclayHedgeCTA":
        # Special calculations for BarclayHedgeCTA
        results_covid.loc[name, 'Total Return'] = qs.stats.comp(subperiod_data_covid)
        results_covid.loc[name, 'Annualized Volatility'] = qs.stats.volatility(subperiod_data_covid, periods=12)
        results_covid.loc[name, 'Sharpe Ratio'] = qs.stats.sharpe(subperiod_data_covid, periods=12)
        results_covid.loc[name, 'Max Drawdown'] = qs.stats.max_drawdown(subperiod_data_covid)
        results_covid.loc[name, 'Sortino Ratio'] = qs.stats.sortino(subperiod_data_covid, periods=12)
        results_covid.loc[name, 'Beta SPY'] = qs.stats.greeks(subperiod_data_covid, benchmark=SPY[start_date:end_date])['beta']
    else:
        # General calculations for all other strategies
        results_covid.loc[name, 'Total Return'] = qs.stats.comp(subperiod_data_covid)
        results_covid.loc[name, 'Annualized Volatility'] = qs.stats.volatility(subperiod_data_covid)
        results_covid.loc[name, 'Sharpe Ratio'] = qs.stats.sharpe(subperiod_data_covid)
        results_covid.loc[name, 'Max Drawdown'] = qs.stats.max_drawdown(subperiod_data_covid)
        results_covid.loc[name, 'Sortino Ratio'] = qs.stats.sortino(subperiod_data_covid)
        if name != 'SPY':  # Assuming you don't want to calculate beta for SPY against itself
            greek = qs.stats.greeks(subperiod_data_covid, benchmark=SPY[start_date:end_date])
            results_covid.loc[name, 'Beta SPY'] = greek['beta']
        if name != 'BarclayHedgeCTA':  # Assuming you don't want to calculate beta for SPY against itself
            greek = qs.stats.greeks(subperiod_data_covid, benchmark=BarclayHedgeCTA[start_date:end_date])
            results_covid.loc[name, 'Beta CTA'] = greek['beta']

# Save results to Excel (optional)
results_covid.to_excel('Results_covid.xlsx')

# Display the results DataFrame
results_covid

<ipython-input-117-f160e715ea37>:52: UserWarning:

Pandas requires version '1.4.3' or newer of 'xlsxwriter' (version '1.3.8' currently installed).

	USD Future	EUR Future (USD)	JPY Future (USD)	GBP Future (USD)	GBP Future (EUR)	CAD Future (USD)	CHF Future (USD)	S&P 500	Nasdaq 100	DJIA	...	Wheat	Corn	Oat	Soybeans	Soybean oil	Frozen orange juice	Cocoa	Coffee	Sugar No.11	Cotton No.2
Date
1999-01-01	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1999-01-04	96.398	1.44314	157.312	153.46	NaN	0.646	115.328	1405.44	NaN	NaN	...	93.92159	19.15196	2.16665	2.94494	0.47548	1.93386	4011.0	29.62163	0.12271	3.47810
1999-01-05	96.449	1.43755	158.465	153.27	NaN	0.651	115.015	1419.31	NaN	NaN	...	94.34238	19.37492	2.19184	2.97325	0.48036	1.97721	4037.0	28.75183	0.13112	3.54881
1999-01-06	97.669	1.41590	155.846	153.27	NaN	0.653	112.996	1449.89	NaN	NaN	...	96.78300	19.95461	2.26239	2.99078	0.48483	1.92255	4026.0	28.38941	0.13187	3.53349
1999-01-07	96.801	1.42891	158.709	152.94	NaN	0.652	114.405	1447.06	NaN	NaN	...	96.78300	19.59788	2.22712	2.95842	0.48199	1.88296	4026.0	28.66727	0.13202	3.46691
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-12-25	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2023-12-26	101.104	1.10840	71.135	127.30	0.87070	0.759	118.205	4825.00	17083.50	37894.0	...	6.36250	4.80250	3.68250	13.19000	0.48510	3.26350	4283.0	1.94350	0.20530	0.80050
2023-12-27	100.654	1.11385	71.420	128.00	0.87020	0.758	119.600	4833.50	17113.25	38006.0	...	6.23000	4.76500	3.73250	13.20500	0.48660	3.25600	4280.0	1.97750	0.20600	0.80530
2023-12-28	100.913	1.11000	71.550	127.32	0.87180	0.757	119.500	4832.25	17090.50	38029.0	...	6.31500	4.74250	3.87250	13.12000	0.47980	3.30200	4256.0	1.98000	0.21760	0.80950
2023-12-29	101.029	1.10750	71.760	127.51	0.86855	0.756	119.800	4820.00	17023.50	38012.0	...	6.28000	4.71250	3.85750	12.98000	0.48180	3.20200	4196.0	1.88300	0.20580	0.81000

	USD Future	EUR Future (USD)	JPY Future (USD)	GBP Future (USD)	GBP Future (EUR)	CAD Future (USD)	CHF Future (USD)	S&P 500	Nasdaq 100	DJIA	...	Wheat	Corn	Oat	Soybeans	Soybean oil	Frozen orange juice	Cocoa	Coffee	Sugar No.11	Cotton No.2
count	6446.000000	6342.000000	6343.000000	6343.000000	6050.000000	6342.000000	6345.000000	6340.000000	6226.000000	5484.000000	...	6298.000000	6297.000000	6298.000000	6296.000000	6299.000000	6315.000000	6271.000000	6270.000000	6270.000000	6309.000000
mean	92.352662	1.365555	119.767115	156.222932	0.879098	0.813842	115.315977	1975.638863	5066.023518	17312.395881	...	28.505759	6.873685	3.009682	5.963804	0.338447	1.447795	3074.741030	5.425810	0.214540	1.130652
std	12.856225	0.205537	21.178401	23.028406	0.069735	0.115822	14.324638	1118.152745	4315.944208	9269.738989	...	19.892358	3.630506	1.023585	2.996857	0.118826	0.454977	682.314146	5.211750	0.090641	0.675171
min	72.249000	0.967680	67.310000	107.610000	0.749890	0.605000	79.487000	618.060000	1005.090000	5608.210000	...	5.610000	2.035280	1.279350	1.858690	0.147050	0.665410	1434.000000	1.044610	0.069590	0.390200
25%	82.774250	1.211080	103.997000	135.690000	0.811760	0.745000	107.990000	1179.110000	1935.532500	9756.257500	...	9.719905	4.374680	2.178068	3.212427	0.239830	1.108480	2598.000000	2.200015	0.151930	0.758490
50%	90.308500	1.336260	118.937000	153.900000	0.883755	0.794000	115.446000	1449.085000	3066.910000	13125.990000	...	24.412540	6.035560	2.802790	5.807535	0.320440	1.432480	2945.000000	4.467575	0.197170	0.907400
75%	98.711000	1.540402	134.561500	171.480000	0.938488	0.916000	123.663000	2578.885000	6705.342500	25092.237500	...	43.393873	8.457120	3.716982	7.723365	0.415310	1.680555	3629.000000	5.875788	0.254560	1.228830
max	129.235000	1.848930	165.729000	214.970000	1.093390	1.068000	176.237000	5044.920000	17733.950000	38282.710000	...	98.045380	20.241920	6.575930	15.340060	0.773210	4.258000	4953.000000	31.145250	0.533340	3.867300

	USD Future	EUR Future (USD)	JPY Future (USD)	GBP Future (USD)	GBP Future (EUR)	CAD Future (USD)	CHF Future (USD)	S&P 500	Nasdaq 100	S&P Canada 60	FTSE 100	DAX	SMI	CAC - 40	IBEX 35	Euro Stoxx 50	Nikkei 225	Hang Seng Index	US 2-year	US 5-year	US 10-year	Long Gilt (10 year)	Euro-Schatz-Futures (2 year)	Euro-Bobl-Future (5 year)	Euro-Bund-Future (10 year)	Euro-Buxl-Future (30 year)	Brent Crude Oil	WTI Crude Oil	Heating Oil	Natural Gas	Gold	Silver	Platinum	Palladium	Copper	Nickel	Zinc	Lean hogs	Live cattle	Feeder cattle	Wheat	Corn	Oat	Soybeans	Soybean oil	Frozen orange juice	Cocoa	Coffee	Sugar No.11	Cotton No.2
Date
2000-01-04	-0.017301	-0.018726	-0.010262	0.011188	0.000000	-0.002950	-0.019341	-0.050080	0.0	0.0	-0.024268	-0.031807	-0.015051	-0.026219	-0.025887	-0.033067	0.002820	0.000763	0.000966	0.000927	0.002986	-0.001479	0.002047	0.003393	-0.016893	0.013577	-0.027977	-0.001862	-0.001331	0.067950	-0.020597	-0.014430	-0.020354	-0.016386	-0.017536	-0.015752	-0.018352	0.020434	-0.007572	0.004970	5.042964e-03	0.007362	2.290204e-03	-0.003716	0.012554	-0.024958	0.000955	-0.079745	-0.058878	0.000196
2000-01-05	-0.017797	-0.020362	-0.021354	0.014146	0.000000	-0.001474	-0.021037	-0.048845	0.0	0.0	-0.041524	-0.043018	-0.006016	-0.058880	-0.048025	-0.053213	-0.036596	-0.081737	0.002303	0.004758	0.010354	0.001378	0.002047	0.003490	-0.017953	0.018382	-0.055324	-0.027244	-0.019638	0.071633	-0.026239	-0.045662	-0.049276	-0.025011	-0.007563	-0.016594	-0.023095	0.010499	-0.009022	0.004386	-5.017507e-03	0.007362	3.295975e-17	-0.015316	0.003753	-0.001178	0.007182	-0.059731	-0.065739	-0.016031
2000-01-06	-0.014901	-0.018433	-0.032261	0.017488	0.000000	-0.008876	-0.019495	-0.055583	0.0	0.0	-0.053399	-0.052706	-0.012579	-0.066145	-0.048025	-0.064771	-0.076551	-0.126826	0.001611	0.003015	0.006800	0.001870	0.001949	0.003490	-0.010969	0.022851	-0.059636	-0.032569	-0.023716	0.058801	-0.025193	-0.037365	-0.069144	-0.031424	-0.011659	-0.023237	-0.020821	0.012306	0.006801	0.011937	1.413800e-16	0.003674	2.290204e-03	-0.012167	-0.001889	-0.014783	-0.004760	-0.074597	-0.058878	-0.026066
2000-01-07	-0.013314	-0.016786	-0.030383	0.012044	-0.003802	-0.005908	-0.014391	-0.016644	0.0	0.0	-0.051391	-0.002766	-0.033208	-0.048580	-0.034360	-0.031181	-0.061580	-0.108566	0.000874	0.000811	0.002986	0.002139	-0.000097	-0.001257	-0.020374	0.010918	-0.082458	-0.055469	-0.047077	0.069330	-0.023414	-0.042801	-0.064069	-0.011863	-0.010489	-0.032734	-0.023095	0.020434	0.018156	0.017712	-1.299355e-02	-0.012151	-2.284971e-03	-0.018976	-0.010564	-0.014188	-0.018904	-0.097974	-0.070954	-0.061528
2000-01-10	-0.011824	-0.014073	-0.029339	0.010925	-0.002061	-0.005908	-0.011585	-0.006254	0.0	0.0	-0.033931	0.012392	-0.035470	-0.033412	-0.032734	-0.021517	-0.061580	-0.067519	0.001980	0.003015	0.005436	-0.004205	-0.000292	-0.002030	-0.017256	0.010210	-0.055324	-0.037065	-0.047511	0.049735	-0.024129	-0.041880	-0.057794	0.006113	-0.019311	-0.038132	-0.033485	0.020879	0.016743	0.016847	-1.992087e-02	-0.019371	-2.284971e-03	-0.007951	0.002478	-0.012401	-0.002383	-0.068624	-0.069252	-0.054088

	USD Future	EUR Future (USD)	JPY Future (USD)	GBP Future (USD)	GBP Future (EUR)	CAD Future (USD)	CHF Future (USD)	S&P 500	Nasdaq 100	S&P Canada 60	FTSE 100	DAX	SMI	CAC - 40	IBEX 35	Euro Stoxx 50	Nikkei 225	Hang Seng Index	US 2-year	US 5-year	US 10-year	Long Gilt (10 year)	Euro-Schatz-Futures (2 year)	Euro-Bobl-Future (5 year)	Euro-Bund-Future (10 year)	Euro-Buxl-Future (30 year)	Brent Crude Oil	WTI Crude Oil	Heating Oil	Natural Gas	Gold	Silver	Platinum	Palladium	Copper	Nickel	Zinc	Lean hogs	Live cattle	Feeder cattle	Wheat	Corn	Oat	Soybeans	Soybean oil	Frozen orange juice	Cocoa	Coffee	Sugar No.11	Cotton No.2
Date
2000-01-04	-0.015201	-0.018726	-0.010262	-0.001316	0.000000	-0.002950	-0.019341	-0.050080	0.0	0.0	-0.024268	-0.031807	-0.015051	-0.026219	-0.025887	-0.033067	0.002820	0.000763	-0.001244	0.000927	0.002986	-0.001479	0.0	0.003393	-0.016893	0.013577	-0.027977	-0.001862	-0.001331	0.067950	-0.020597	-0.014430	-0.020354	-0.016386	-0.017536	-0.015752	-0.018352	0.020434	-0.007572	0.004970	5.042964e-03	0.007362	2.290204e-03	-0.003716	0.012554	-0.024958	0.000955	-0.079745	-0.058878	0.000196
2000-01-05	-0.015201	-0.020362	-0.021354	0.001642	0.000000	-0.001474	-0.021037	-0.048845	0.0	0.0	-0.041524	-0.043018	-0.015051	-0.058880	-0.048025	-0.053213	-0.036596	-0.081737	0.000092	0.004758	0.010354	0.001378	0.0	0.003490	-0.017953	0.018382	-0.055324	-0.027244	-0.019638	0.071633	-0.020597	-0.045662	-0.049276	-0.025011	-0.007563	-0.016594	-0.023095	0.010499	-0.009022	0.004386	-5.017507e-03	0.007362	3.295975e-17	-0.015316	0.003753	-0.001178	0.007182	-0.059731	-0.065739	-0.016031
2000-01-06	-0.015201	-0.018433	-0.032261	0.004984	0.000000	-0.008876	-0.019495	-0.055583	0.0	0.0	-0.053399	-0.052706	-0.021614	-0.066145	-0.048025	-0.064771	-0.076551	-0.126826	-0.000599	0.003015	0.006800	0.001870	0.0	0.003490	-0.010969	0.022851	-0.059636	-0.032569	-0.023716	0.058801	-0.020597	-0.037365	-0.069144	-0.031424	-0.011659	-0.023237	-0.020821	0.012306	0.006801	0.011937	1.413800e-16	0.003674	2.290204e-03	-0.012167	-0.001889	-0.014783	-0.004760	-0.074597	-0.058878	-0.026066
2000-01-07	-0.015201	-0.016786	-0.030383	-0.000460	-0.003802	-0.008876	-0.014391	-0.016644	0.0	0.0	-0.051391	-0.002766	-0.021614	-0.048580	-0.034360	-0.031181	-0.061580	-0.108566	-0.001336	0.000811	0.002986	0.002139	0.0	-0.001257	-0.020374	0.010918	-0.082458	-0.055469	-0.047077	0.069330	-0.020597	-0.042801	-0.064069	-0.011863	-0.010489	-0.032734	-0.023095	0.020434	0.018156	0.017712	-1.299355e-02	-0.012151	-2.284971e-03	-0.018976	-0.010564	-0.014188	-0.018904	-0.097974	-0.070954	-0.061528
2000-01-10	-0.015201	-0.014073	-0.029339	-0.001579	-0.002061	-0.008876	-0.011585	-0.006254	0.0	0.0	-0.033931	0.012392	-0.023876	-0.033412	-0.032734	-0.021517	-0.061580	-0.067519	-0.000230	0.003015	0.005436	-0.004205	0.0	-0.002030	-0.017256	0.010210	-0.055324	-0.037065	-0.047511	0.049735	-0.020597	-0.041880	-0.057794	0.006113	-0.019311	-0.038132	-0.033485	0.020879	0.016743	0.016847	-1.992087e-02	-0.019371	-2.284971e-03	-0.007951	0.002478	-0.012401	-0.002383	-0.068624	-0.069252	-0.054088

	USD Future	EUR Future (USD)	JPY Future (USD)	GBP Future (USD)	GBP Future (EUR)	CAD Future (USD)	CHF Future (USD)	S&P 500	Nasdaq 100	S&P Canada 60	FTSE 100	DAX	SMI	CAC - 40	IBEX 35	Euro Stoxx 50	Nikkei 225	Hang Seng Index	US 2-year	US 5-year	US 10-year	Long Gilt (10 year)	Euro-Schatz-Futures (2 year)	Euro-Bobl-Future (5 year)	Euro-Bund-Future (10 year)	Euro-Buxl-Future (30 year)	Brent Crude Oil	WTI Crude Oil	Heating Oil	Natural Gas	Gold	Silver	Platinum	Palladium	Copper	Nickel	Zinc	Lean hogs	Live cattle	Feeder cattle	Wheat	Corn	Oat	Soybeans	Soybean oil	Frozen orange juice	Cocoa	Coffee	Sugar No.11	Cotton No.2
Date
2000-01-04	-0.013101	-0.018726	-0.010262	-0.011188	0.0	-0.002950	-0.019341	-0.050080	0.0	0.0	0.000000	-0.015641	0.015051	-0.026219	0.000000	-0.029524	0.0	0.0	0.000000	0.000000e+00	0.000000	-0.001479	-0.000195	0.000000	-0.008789	0.013577	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	-0.015752	-0.018352	-0.006274	-0.009009	-0.001166	0.000000	0.000000	0.000000	0.000000	0.000000	0.024958	0.000000	0.000000	0.000000	0.000196
2000-01-05	-0.013597	-0.020362	-0.021354	-0.014146	0.0	-0.001474	-0.021037	-0.048845	0.0	0.0	-0.017256	-0.026852	0.024086	-0.058880	0.000000	-0.049670	0.0	0.0	0.000000	0.000000e+00	0.000000	0.001378	-0.000195	0.000097	-0.009848	0.018382	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	-0.016594	-0.023095	-0.016209	-0.010459	-0.001749	0.000000	0.000000	0.000000	0.000000	0.000000	0.001178	0.000000	0.000000	0.000000	-0.016031
2000-01-06	-0.010701	-0.018433	-0.032261	-0.017488	0.0	-0.008876	-0.019495	-0.055583	0.0	0.0	-0.029131	-0.036540	0.017523	-0.066145	0.000000	-0.061229	0.0	0.0	0.000000	0.000000e+00	-0.003554	0.001870	-0.000293	0.000097	-0.002864	0.022851	-4.311384e-03	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	-0.023237	-0.020821	-0.014402	0.005363	0.005801	0.005018	-0.003688	0.000000	0.000000	0.000000	0.014783	0.000000	0.000000	0.000000	-0.026066
2000-01-07	-0.009114	-0.016786	-0.030383	-0.012044	0.0	-0.005908	-0.014391	-0.016644	0.0	0.0	-0.027124	0.013400	-0.003107	-0.048580	0.000000	-0.027638	0.0	0.0	-0.000737	-2.204049e-03	-0.007368	0.002139	-0.002339	-0.004651	-0.012269	0.010918	-2.713345e-02	0.000000	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	-0.032734	-0.023095	-0.006274	0.016719	0.011576	-0.007976	-0.019513	-0.004575	-0.006809	-0.008676	0.014188	-0.014145	0.023378	-0.012076	-0.061528
2000-01-10	-0.007624	-0.014073	-0.029339	-0.010925	0.0	-0.005908	-0.011585	-0.006254	0.0	0.0	-0.009663	0.028558	-0.000845	-0.033412	-0.001626	-0.017974	0.0	0.0	0.000369	4.466913e-17	-0.004918	-0.004205	-0.002534	-0.005424	-0.009151	0.010210	3.122502e-17	0.018405	-0.000434	-0.019595	0.000715	0.0	0.0	0.0	0.0	-0.038132	-0.033485	-0.005829	0.015306	0.010712	-0.014903	-0.026733	-0.004575	0.004216	0.004366	0.012401	0.002377	-0.005973	-0.010374	-0.054088

	USD Future	EUR Future (USD)	JPY Future (USD)	GBP Future (USD)	GBP Future (EUR)	CAD Future (USD)	CHF Future (USD)	S&P 500	Nasdaq 100	S&P Canada 60	FTSE 100	DAX	SMI	CAC - 40	IBEX 35	Euro Stoxx 50	Nikkei 225	Hang Seng Index	US 2-year	US 5-year	US 10-year	Long Gilt (10 year)	Euro-Schatz-Futures (2 year)	Euro-Bobl-Future (5 year)	Euro-Bund-Future (10 year)	Euro-Buxl-Future (30 year)	Brent Crude Oil	WTI Crude Oil	Heating Oil	Natural Gas	Gold	Silver	Platinum	Palladium	Copper	Nickel	Zinc	Lean hogs	Live cattle	Feeder cattle	Wheat	Corn	Oat	Soybeans	Soybean oil	Frozen orange juice	Cocoa	Coffee	Sugar No.11	Cotton No.2
Date
1999-12-02	0.004590	0.006265	0.000000	4.709523e-04	0.0	-0.004508	0.004931	0.006932	0.0	0.0	0.001339	0.000488	-0.001752	-0.005761	0.009062	-0.001177	-0.008317	0.007219	0.000413	9.188010e-04	0.001871	0.005999	0.001356	0.002393	0.008732	0.008788	0.038264	0.032342	0.032365	-0.028020	-0.016580	0.016017	-0.005473	0.012976	-0.008719	0.011721	-0.012531	0.001436	-0.004316	-0.000593	0.017043	0.001234	-6.706804e-03	0.006853	0.016702	0.009761	2.330022e-02	3.823561e-02	0.001726	0.003552
1999-12-03	0.004692	0.006566	-0.000616	1.544988e-17	0.0	-0.006013	0.002384	0.024825	0.0	0.0	0.014686	0.025189	0.006006	0.011870	0.022211	0.018092	-0.006757	0.026087	-0.001191	-2.865824e-03	-0.002934	0.009766	0.000290	-0.000287	0.005635	0.001889	0.037795	0.031929	0.028755	0.026250	-0.034136	0.014052	-0.008097	0.018845	0.008030	0.043510	0.006418	0.008575	-0.005046	0.004728	0.035411	0.006184	-8.930772e-03	0.007379	0.026386	0.013333	3.754547e-02	9.610882e-02	-0.037425	0.017286
1999-12-06	-0.009850	-0.013086	-0.004212	-1.130333e-02	0.0	-0.003010	-0.014956	0.016267	0.0	0.0	0.022056	0.049985	0.008898	0.041281	0.022211	0.052100	-0.008525	0.044097	-0.001511	-3.323595e-03	-0.004530	-0.006353	-0.000584	-0.002783	-0.021539	-0.010580	0.071039	0.064272	0.047223	0.073240	-0.020204	0.012092	-0.010256	0.039639	0.010483	0.051454	0.019138	0.003828	-0.006482	0.000296	0.015023	-0.008595	-2.217666e-02	-0.001050	0.033687	0.008739	2.330022e-02	2.788417e-02	0.003538	0.020496
1999-12-07	-0.012128	-0.015335	0.002352	-7.983498e-03	0.0	-0.003010	-0.016355	0.009406	0.0	0.0	0.017793	0.050992	0.004148	0.045323	0.059892	0.050696	0.003030	0.040656	-0.002015	-5.266786e-03	-0.007716	-0.015174	-0.000195	-0.003166	-0.022935	-0.014139	0.055478	0.047722	0.024201	0.052328	-0.045753	0.004799	-0.009064	0.033908	-0.004972	0.012733	0.003426	-0.003355	-0.004685	0.003548	0.023128	-0.002463	-1.777751e-02	-0.008886	0.023344	0.002066	-6.885499e-03	2.079859e-02	-0.022492	0.005927
1999-12-08	-0.013716	-0.017961	-0.003079	-5.864573e-03	0.0	-0.006013	-0.018387	0.002141	0.0	0.0	0.013134	0.050758	0.000955	0.045421	0.059892	0.057004	-0.015532	0.030574	-0.001420	-3.781156e-03	-0.006390	-0.015793	0.000388	-0.001727	-0.025188	-0.011259	0.066943	0.059657	0.036606	0.044869	-0.050322	0.010135	-0.008816	0.036842	-0.008719	0.018412	-0.001718	-0.006233	0.003567	0.011185	0.039539	0.013656	-6.706804e-03	0.008438	0.033093	-0.009334	6.071532e-18	-1.346433e-02	-0.029058	0.013685
1999-12-09	-0.010343	-0.011718	0.001225	-7.983498e-03	0.0	-0.009016	-0.012299	0.005686	0.0	0.0	0.020884	0.044052	-0.003758	0.048473	0.058859	0.056264	-0.020567	0.060987	-0.001877	-4.809904e-03	-0.007981	-0.019453	-0.000584	-0.004411	-0.022890	-0.013631	0.042011	0.044869	0.013493	0.046182	-0.063786	0.012504	-0.016261	0.034150	-0.003099	0.024182	0.004712	-0.017375	-0.002164	0.007958	0.043684	0.027502	-4.477880e-03	0.035280	0.064762	-0.001551	-2.051588e-02	-6.798358e-02	-0.017500	0.017286
1999-12-10	-0.003536	-0.002568	0.001944	-1.043912e-02	0.0	-0.010516	-0.002857	0.010113	0.0	0.0	0.027664	0.033574	-0.019819	0.028262	0.038236	0.033632	-0.017422	0.059040	-0.002746	-7.320175e-03	-0.012739	-0.024332	-0.001554	-0.006992	-0.021551	-0.021218	0.017879	0.009136	-0.015226	-0.021906	-0.066279	0.010135	-0.005226	0.057995	-0.013099	0.015262	-0.003657	-0.026665	-0.015911	-0.002075	0.039539	0.022445	-4.477880e-03	0.032021	0.066023	-0.021395	-8.256928e-03	-2.986440e-02	-0.029058	0.016283
1999-12-13	-0.004234	-0.004428	-0.006763	-8.381297e-03	0.0	-0.012019	-0.004106	0.010287	0.0	0.0	0.024431	0.035195	-0.017834	0.035801	0.042070	0.037825	-0.032808	0.055331	-0.002106	-6.179924e-03	-0.011155	-0.023580	-0.000778	-0.004698	-0.014199	-0.015322	0.022657	0.015041	0.007606	-0.047337	-0.070201	0.010546	0.007788	0.074471	-0.006217	0.010476	-0.000893	-0.033914	-0.019564	-0.003263	0.056223	0.035135	9.006133e-03	0.053949	0.080686	-0.037343	3.277447e-02	-5.095131e-02	-0.030706	0.015082
1999-12-14	0.002560	0.004031	-0.010122	-1.610334e-02	0.0	-0.010514	0.003735	0.002426	0.0	0.0	0.016421	0.043739	-0.013049	0.036612	0.038449	0.039288	-0.036059	0.046346	0.000138	-1.605873e-03	-0.003200	-0.011770	0.000290	-0.002591	-0.003716	-0.003254	0.047605	0.028823	0.028136	-0.077178	-0.069833	-0.002340	0.005908	0.087685	0.002481	0.014131	0.007152	-0.014557	-0.016275	-0.003263	0.041609	0.018669	4.336809e-17	0.049525	0.074272	-0.052461	2.897399e-02	-6.594041e-02	-0.054272	0.012686
1999-12-15	0.002756	0.002649	-0.012164	-1.161588e-02	0.0	-0.009007	0.002164	0.004879	0.0	0.0	0.002936	0.044478	-0.008180	0.026054	0.027260	0.032383	-0.040092	0.017607	0.000917	-3.816392e-17	-0.000267	-0.010441	-0.000486	-0.004890	-0.007365	-0.010071	0.066031	0.052999	0.048254	-0.038127	-0.079018	-0.001425	-0.012171	0.087914	0.040108	0.036152	0.016383	-0.006121	-0.018101	-0.002966	0.041609	0.018669	1.581952e-02	0.050079	0.074272	-0.070027	1.718974e-02	-1.929078e-02	-0.030138	0.021100
1999-12-16	-0.004234	-0.006779	-0.006456	-1.510043e-02	0.0	-0.013521	-0.005509	0.010808	0.0	0.0	0.010410	0.071117	-0.021820	0.037582	0.040593	0.047345	-0.033427	0.010303	0.002110	2.183532e-03	0.004551	0.000842	0.003014	0.002987	-0.001683	0.011561	0.089933	0.070656	0.069578	-0.096715	-0.081826	-0.006194	-0.007626	0.107454	0.041296	0.044365	0.034597	-0.013213	-0.016275	-0.003263	0.031300	0.013656	3.189332e-02	0.053397	0.085194	-0.049204	9.255041e-03	-4.406198e-16	-0.031968	0.037936
1999-12-17	0.001876	0.001760	-0.007777	-1.134733e-02	0.0	-0.015024	0.004049	0.015666	0.0	0.0	0.017015	0.073383	-0.020706	0.033335	0.049056	0.052026	-0.033061	0.030583	0.002294	2.643832e-03	0.004819	0.003679	0.002819	0.003855	0.008547	0.016955	0.092603	0.067270	0.074640	-0.103897	-0.075872	-0.008418	-0.022779	0.116741	0.046635	0.041920	0.025529	-0.015893	-0.015911	-0.005643	0.021096	0.006184	2.727066e-02	0.051184	0.087769	-0.064504	3.134760e-02	1.177610e-02	-0.054272	0.032427
1999-12-20	-0.001569	-0.001682	-0.000885	-1.195146e-02	0.0	-0.015024	-0.001909	0.009245	0.0	0.0	0.017581	0.072012	-0.024185	0.029676	0.055017	0.058351	-0.028240	0.046758	0.002892	4.833151e-03	0.008853	0.004440	0.002527	0.004241	0.010384	0.019926	0.084122	0.060461	0.079558	-0.094056	-0.074112	-0.003254	-0.024012	0.111317	0.048405	0.034552	0.018888	-0.022164	-0.013361	-0.005941	0.009990	-0.002463	2.727066e-02	0.037458	0.074926	-0.051374	4.957352e-02	3.344288e-02	-0.067791	0.034058
1999-12-21	-0.000977	0.001270	0.001542	-1.610334e-02	0.0	-0.016527	0.001223	0.022738	0.0	0.0	0.019897	0.080497	-0.015171	0.033642	0.046367	0.049064	-0.019231	0.043853	0.003121	5.525512e-03	0.010471	-0.000081	0.001554	0.001541	0.003443	0.009307	0.083674	0.057443	0.075230	-0.052505	-0.064033	0.003367	-0.009064	0.107799	0.032332	0.027998	0.015964	-0.016788	-0.008290	0.001776	0.022111	0.002469	2.267445e-02	0.041284	0.076819	-0.057917	4.957352e-02	-8.074367e-02	-0.074479	0.026747
1999-12-22	-0.001174	0.001071	0.005463	-1.349369e-02	0.0	-0.018029	0.002007	0.024800	0.0	0.0	0.016285	0.088800	-0.014538	0.051278	0.047984	0.069496	0.002108	0.042255	0.003305	5.871872e-03	0.011011	0.010945	0.002333	0.002601	0.007902	0.011387	0.057781	0.027993	0.058148	-0.021088	-0.064714	-0.009428	0.015056	0.112228	0.056642	0.054202	0.033776	-0.021264	-0.008652	0.002071	0.014014	-0.003693	2.267445e-02	0.019091	0.052209	-0.066159	7.011214e-02	-1.063375e-01	-0.069335	0.028771
1999-12-23	-0.006603	-0.004527	0.004261	-1.757246e-02	0.0	-0.024026	-0.001284	0.040459	0.0	0.0	0.028903	0.141231	-0.032359	0.076650	0.065232	0.097409	0.002108	0.049631	0.003397	6.564953e-03	0.012092	0.004863	0.002138	0.002023	0.000030	0.008615	0.079630	0.042417	0.066070	-0.002505	-0.065415	-0.005586	0.006372	0.118212	0.055472	0.053961	0.035828	-0.019023	-0.004325	0.007664	0.006983	-0.007371	2.038425e-02	0.019091	0.049121	-0.065052	2.094561e-02	-7.619713e-02	-0.067647	0.020496
1999-12-24	-0.004328	-0.004527	0.004261	-1.757246e-02	0.0	-0.024026	-0.001284	0.040459	0.0	0.0	0.033588	0.141231	-0.032359	0.091136	0.065232	0.097409	-0.001501	0.083227	0.003397	6.564953e-03	0.012092	0.004477	0.002138	0.002023	0.000030	0.008615	0.080530	0.042417	0.066070	-0.002505	-0.065415	-0.005586	0.006372	0.118212	0.055472	0.069435	0.026564	-0.019023	-0.004325	0.007664	0.006983	-0.007371	2.038425e-02	0.019091	0.049121	-0.065052	2.094561e-02	-7.619713e-02	-0.067647	0.020496
1999-12-27	-0.004724	-0.002469	-0.001490	-1.557259e-02	0.0	-0.016580	0.001693	0.038599	0.0	0.0	0.033588	0.149890	-0.030447	0.086506	0.059885	0.103571	0.002690	0.083227	0.002846	5.871872e-03	0.011281	0.004477	0.001360	0.001252	-0.000144	0.007404	0.080530	0.060059	0.084014	0.040127	-0.060916	-0.007104	0.001646	0.129295	0.054300	0.069530	0.026854	-0.019023	-0.005046	0.004433	0.018055	-0.006146	2.038425e-02	0.037458	0.066670	-0.079480	1.251465e-02	-6.880187e-02	-0.074479	0.038347
1999-12-28	-0.001174	0.004323	-0.002198	-1.697208e-02	0.0	-0.009189	0.009264	0.039273	0.0	0.0	0.033588	0.139680	-0.028994	0.079541	0.042981	0.092428	0.012125	0.090569	0.003305	7.142888e-03	0.013445	0.004477	0.002333	0.003951	0.011873	0.013646	0.080530	0.078580	0.098821	0.008828	-0.055066	-0.004674	0.015298	0.129750	0.054886	0.069530	0.026854	-0.041207	-0.005046	0.004728	0.017043	-0.009816	2.267445e-02	0.038006	0.071012	-0.076694	3.515712e-02	-9.492794e-02	-0.074479	0.037119
1999-12-29	-0.000873	0.005414	-0.000378	-1.510653e-02	0.0	-0.013617	0.010529	0.041637	0.0	0.0	0.039158	0.141753	-0.033266	0.079758	0.048237	0.100643	0.012289	0.071236	0.002662	6.102846e-03	0.011822	0.002788	0.002138	0.004048	0.013437	0.013820	0.089933	0.065472	0.093272	-0.001670	-0.051977	-0.037091	0.022512	0.141910	0.062481	0.065499	0.046017	-0.050871	-0.004325	0.006488	0.019067	-0.004920	2.038425e-02	0.045533	0.080375	-0.067811	2.566038e-02	-8.655975e-02	-0.110910	0.023112

	USD Future	EUR Future (USD)	JPY Future (USD)	GBP Future (USD)	GBP Future (EUR)	CAD Future (USD)	CHF Future (USD)	S&P 500	Nasdaq 100	S&P Canada 60	FTSE 100	DAX	SMI	CAC - 40	IBEX 35	Euro Stoxx 50	Nikkei 225	Hang Seng Index	US 2-year	US 5-year	US 10-year	Long Gilt (10 year)	Euro-Schatz-Futures (2 year)	Euro-Bobl-Future (5 year)	Euro-Bund-Future (10 year)	Euro-Buxl-Future (30 year)	Brent Crude Oil	WTI Crude Oil	Heating Oil	Natural Gas	Gold	Silver	Platinum	Palladium	Copper	Nickel	Zinc	Lean hogs	Live cattle	Feeder cattle	Wheat	Corn	Oat	Soybeans	Soybean oil	Frozen orange juice	Cocoa	Coffee	Sugar No.11	Cotton No.2
Date
1999-12-02	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-03	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-06	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-07	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-08	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-09	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-10	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-13	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-14	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-15	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-16	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-17	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-20	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-21	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-22	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-23	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-24	0.00000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-27	0.00000	0.002058	-0.005751	-0.002000	0.0	0.007446	0.002977	-0.001861	0.0	0.0	0.0	0.0	0.0	-0.004630	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000094	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000
1999-12-28	0.00355	0.008849	-0.006459	-0.000600	0.0	0.014837	0.010547	-0.001186	0.0	0.0	0.0	0.0	0.0	-0.011595	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000094	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.002786	0.0	0.0	0.0	0.000000
1999-12-29	0.00385	0.009941	-0.004639	-0.002466	0.0	0.010409	0.011813	0.001178	0.0	0.0	0.0	0.0	0.0	-0.011378	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.003936	0.019164	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.011669	0.0	0.0	0.0	-0.014007

	USD Future	EUR Future (USD)	JPY Future (USD)	GBP Future (USD)	GBP Future (EUR)	CAD Future (USD)	CHF Future (USD)	S&P 500	Nasdaq 100	S&P Canada 60	FTSE 100	DAX	SMI	CAC - 40	IBEX 35	Euro Stoxx 50	Nikkei 225	Hang Seng Index	US 2-year	US 5-year	US 10-year	Long Gilt (10 year)	Euro-Schatz-Futures (2 year)	Euro-Bobl-Future (5 year)	Euro-Bund-Future (10 year)	Euro-Buxl-Future (30 year)	Brent Crude Oil	WTI Crude Oil	Heating Oil	Natural Gas	Gold	Silver	Platinum	Palladium	Copper	Nickel	Zinc	Lean hogs	Live cattle	Feeder cattle	Wheat	Corn	Oat	Soybeans	Soybean oil	Frozen orange juice	Cocoa	Coffee	Sugar No.11	Cotton No.2
Date
1999-12-02	-0.004590	-0.006265	0.000000	-4.709523e-04	-0.005719	-4.507896e-03	-0.004931	-0.006932	0.032159	0.006189	0.001339	-0.000488	-0.001752	-0.005761	-0.009062	-0.001177	-0.008317	-0.007219	-0.000413	-0.000919	-0.001871	-0.005999	0.001356	0.002393	0.008732	0.008788	0.038264	0.032342	0.032365	-0.028020	-0.016580	-0.016017	0.005473	0.012976	-0.008719	0.011721	-0.012531	0.001436	-0.004316	-0.000593	0.017043	0.001234	6.706804e-03	0.006853	0.016702	-0.009761	-0.023300	-0.038236	-0.001726	-0.003552
1999-12-03	-0.004692	-0.006566	-0.000616	-1.544988e-17	-0.006513	-3.003005e-03	-0.002384	-0.024825	0.012592	-0.011404	-0.012007	-0.025189	-0.009509	0.011870	-0.022211	0.018092	-0.006757	-0.026087	0.001191	0.002866	0.002934	-0.009766	0.002422	0.005073	0.011830	0.015687	0.038733	0.032756	0.035975	0.026250	-0.034136	-0.014052	0.008097	0.007108	0.008030	-0.020068	0.006418	0.008575	-0.005046	0.004728	-0.001325	0.006184	4.482837e-03	0.006326	0.007017	-0.013333	-0.037545	-0.096109	0.037425	-0.017286
1999-12-06	0.009850	0.013086	-0.004212	-1.130333e-02	0.000952	4.553649e-17	-0.019724	-0.016267	0.003031	-0.022353	-0.019377	-0.049985	-0.012402	-0.017542	-0.022211	-0.015915	-0.008525	-0.044097	0.000871	0.002408	0.001337	0.006353	0.001548	0.002577	0.039004	0.003218	0.005489	0.000413	0.017507	-0.020740	-0.048068	-0.012092	0.005938	-0.013687	0.005578	-0.028011	-0.006301	0.013323	-0.006482	0.009159	0.019063	0.020963	-8.763051e-03	0.014755	-0.000284	-0.008739	-0.023300	-0.027884	-0.003538	-0.020496
1999-12-07	0.007571	0.010837	0.002352	-1.462317e-02	0.001265	4.553649e-17	-0.021122	-0.023128	0.000847	-0.025613	-0.015115	-0.050992	-0.007651	-0.021584	-0.059892	-0.014511	0.003030	-0.040656	0.000367	0.000465	-0.001848	-0.002468	0.001936	0.002194	0.037608	-0.000341	0.021050	0.016962	0.040529	0.000172	-0.022519	-0.019384	0.007130	-0.007955	0.021032	0.010709	0.009411	0.006139	-0.004685	0.012411	0.027169	0.027094	-4.363900e-03	0.006919	0.010060	-0.015412	-0.053486	-0.034970	0.022492	-0.005927
1999-12-08	0.005983	0.008210	0.007783	-1.674210e-02	0.000627	3.003005e-03	-0.023154	-0.030393	0.015432	-0.012388	-0.010456	-0.050758	-0.010844	-0.021681	-0.059892	-0.020820	0.021592	-0.030574	0.000962	0.001950	-0.000522	-0.003087	0.002519	0.003632	0.035355	0.002539	0.032516	0.028897	0.052935	0.007631	-0.017950	-0.014048	0.006882	-0.010890	0.017285	0.016388	0.004267	0.003261	-0.012937	0.004774	0.010758	0.010975	6.706804e-03	0.024243	0.019809	-0.026812	-0.046600	-0.069233	0.015926	0.001831
1999-12-09	0.009356	0.014454	0.012088	-1.462317e-02	0.004753	6.006011e-03	-0.017066	-0.026847	0.016221	-0.012726	-0.002706	-0.044052	-0.015557	-0.024734	-0.058859	-0.020079	0.016556	-0.000162	0.001420	0.002979	-0.002113	-0.006748	0.003490	0.006316	0.037654	0.000167	0.057448	0.043685	0.076048	0.008944	-0.004486	-0.016418	0.014326	-0.008198	0.022905	0.022158	0.010696	-0.007880	-0.007206	0.008001	0.006613	-0.002871	4.477880e-03	-0.002599	-0.011860	-0.019029	-0.067116	-0.123752	0.027484	-0.001770
1999-12-10	0.002550	0.005304	0.011369	-1.707879e-02	-0.001956	7.506386e-03	-0.026508	-0.022420	0.028614	0.005038	-0.009485	-0.054529	-0.031618	-0.004523	-0.038236	0.002552	0.019702	0.001786	0.000551	0.000469	-0.006872	-0.011626	0.002520	0.003735	0.038993	-0.007420	0.033316	0.007952	0.047330	-0.059145	-0.001993	-0.014048	0.025361	0.015647	0.032905	0.031078	0.019065	-0.017170	-0.020953	-0.002032	0.010758	0.002186	4.477880e-03	0.000660	-0.013120	0.000815	-0.054857	-0.085633	0.039043	-0.000767
1999-12-13	0.003247	0.007164	0.020076	-1.502097e-02	-0.002272	6.003756e-03	-0.025259	-0.022594	0.018223	0.000439	-0.006253	-0.052908	-0.033603	0.003016	-0.034402	0.006745	0.004316	0.005495	0.001191	0.001609	-0.005288	-0.010874	0.003296	0.006029	0.031642	-0.001524	0.038094	0.013857	0.070162	-0.084575	0.001929	-0.013637	0.012346	-0.000828	0.039787	0.026292	0.021829	-0.024420	-0.024607	-0.003220	-0.005927	-0.010504	-9.006133e-03	-0.021269	-0.027784	-0.015133	-0.095889	-0.064546	0.037394	0.000433
1999-12-14	-0.003546	-0.001295	0.016717	-7.298923e-03	-0.002905	4.498865e-03	-0.033100	-0.014733	0.038170	0.009319	0.001758	-0.044365	-0.038389	0.002205	-0.038022	0.008208	0.001065	-0.003490	-0.001053	-0.002965	0.002667	0.000936	0.002228	0.003921	0.021158	-0.013592	0.063042	0.027639	0.049632	-0.114417	0.002297	-0.026523	0.014227	-0.014043	0.031090	0.029948	0.013784	-0.005063	-0.021318	-0.003220	0.008688	0.005962	6.245005e-17	-0.016844	-0.021370	-0.030251	-0.092088	-0.079535	0.013829	-0.001963
1999-12-15	-0.003743	0.000087	0.014676	-1.178639e-02	0.002967	2.991706e-03	-0.031529	-0.012281	0.054018	0.015658	-0.011728	-0.045104	-0.043257	0.012763	-0.049212	0.001303	-0.002968	-0.032229	-0.001832	-0.004571	-0.000265	-0.000393	0.003005	0.006220	0.024807	-0.006775	0.044617	0.003462	0.029514	-0.075366	-0.006888	-0.025608	0.032305	-0.014272	-0.006537	0.007926	0.004553	-0.013499	-0.023144	-0.002923	0.008688	0.005962	1.581952e-02	-0.017399	-0.021370	-0.047817	-0.080304	-0.032885	-0.010305	0.006451
1999-12-16	0.003247	0.009515	0.020383	-8.301831e-03	-0.002871	7.506386e-03	-0.023856	-0.006352	0.022021	0.006992	-0.004254	-0.071744	-0.029617	0.024291	-0.035879	0.016265	0.003697	-0.039533	-0.003026	-0.006754	-0.005083	-0.011676	0.006505	0.014097	0.019125	0.014856	0.020715	-0.014195	0.008190	-0.016778	-0.004080	-0.030377	0.036849	-0.033812	-0.007725	-0.000286	-0.013661	-0.006407	-0.021318	-0.003220	-0.001622	0.000950	-2.542851e-04	-0.020717	-0.032292	-0.026994	-0.088239	-0.052176	-0.008474	-0.010386
1999-12-17	0.009357	0.018053	0.021704	-4.548727e-03	0.001859	9.009016e-03	-0.014297	-0.011210	0.012751	-0.001924	-0.010859	-0.074010	-0.028503	0.028539	-0.044342	0.011583	0.003331	-0.019253	-0.003209	-0.007215	-0.005352	-0.014513	0.006700	0.013229	0.008896	0.009462	0.018045	-0.010808	0.003128	-0.023961	-0.010033	-0.032601	0.021697	-0.043099	-0.013064	0.002159	-0.004593	-0.009087	-0.021683	-0.005600	-0.011826	-0.006522	4.368376e-03	-0.018503	-0.034867	-0.011694	-0.066146	-0.063952	0.013829	-0.004877
1999-12-20	0.012802	0.021496	0.014812	-3.944599e-03	0.004698	9.009016e-03	-0.008339	-0.004789	0.003129	-0.003784	-0.011425	-0.072638	-0.031982	0.024880	-0.050303	0.005258	-0.001491	-0.035429	-0.003807	-0.009404	-0.009386	-0.015275	0.006992	0.012842	0.007059	0.006491	0.026525	-0.004000	-0.001790	-0.014119	-0.011793	-0.027437	0.020463	-0.037675	-0.014834	0.009526	-0.011234	-0.015358	-0.024232	-0.005898	-0.022932	-0.015170	4.368376e-03	-0.032229	-0.022024	0.001436	-0.084372	-0.085619	0.000310	-0.006507
1999-12-21	0.013394	0.024448	0.012385	2.072856e-04	0.011822	1.051165e-02	-0.005207	0.008704	-0.040167	-0.023727	-0.013741	-0.081123	-0.022969	0.028846	-0.041653	0.014546	-0.010500	-0.032523	-0.004037	-0.010096	-0.011004	-0.010754	0.007965	0.015542	0.013999	0.017110	0.026077	-0.007018	0.002538	0.027432	-0.021872	-0.034058	0.035411	-0.034157	0.001239	0.002973	-0.014157	-0.009982	-0.029304	0.001819	-0.010810	-0.010238	8.964579e-03	-0.028403	-0.020130	0.007979	-0.084372	0.028568	0.006997	0.000803
1999-12-22	0.013197	0.024647	0.008464	2.816943e-03	0.014685	1.201428e-02	-0.005991	0.006643	-0.045847	-0.023696	-0.010129	-0.089427	-0.023603	0.011210	-0.040036	0.034977	-0.031839	-0.030925	-0.004220	-0.010443	-0.011544	0.000272	0.008744	0.016602	0.018457	0.019190	0.000184	-0.036468	0.019620	-0.003984	-0.021191	-0.021263	0.011291	-0.029728	0.025549	0.029177	0.003654	-0.005505	-0.028942	0.001523	-0.002713	-0.004076	8.964579e-03	-0.050597	-0.044740	-0.000262	-0.104911	0.002974	0.012142	0.002827
1999-12-23	0.007768	0.030245	0.009666	6.895715e-03	0.013263	1.801130e-02	-0.002701	-0.009017	-0.052594	-0.039131	0.002489	-0.141858	-0.005781	-0.014162	-0.022788	0.007065	-0.031839	-0.038302	-0.004312	-0.011136	-0.012625	0.006355	0.008938	0.017180	0.026330	0.016418	0.022033	-0.022044	0.027543	-0.022568	-0.020490	-0.017422	0.019975	-0.035712	0.026719	0.029418	0.001601	-0.003265	-0.033269	-0.004069	-0.009745	-0.007755	6.674376e-03	-0.050597	-0.047829	0.000845	-0.055744	0.033114	0.010454	-0.005448
1999-12-24	0.010043	0.030245	0.009666	6.895715e-03	0.013263	1.801130e-02	-0.002701	-0.009017	-0.052594	-0.044463	-0.002195	-0.141858	-0.005781	-0.028648	-0.022788	0.007065	-0.028230	-0.071897	-0.004312	-0.011136	-0.012625	0.005969	0.008938	0.017180	0.026330	0.016418	0.021132	-0.022044	0.027543	-0.022568	-0.020490	-0.017422	0.019975	-0.035712	0.026719	0.013943	0.010866	-0.003265	-0.033269	-0.004069	-0.009745	-0.007755	6.674376e-03	-0.050597	-0.047829	0.000845	-0.055744	0.033114	0.010454	-0.005448
1999-12-27	0.010439	0.032303	0.015417	4.895848e-03	0.017220	1.056524e-02	0.000276	-0.007156	-0.051494	-0.044463	-0.002195	-0.150516	-0.003869	-0.024018	-0.017442	0.000902	-0.032420	-0.071897	-0.003761	-0.010443	-0.011814	0.005969	0.008160	0.016410	0.026156	0.015207	0.021132	-0.039686	0.009599	-0.065200	-0.024990	-0.015904	0.024701	-0.046795	0.027891	0.013849	0.011155	-0.003265	-0.032547	-0.000838	0.001327	-0.006530	6.674376e-03	-0.032229	-0.030279	-0.013584	-0.064175	0.025719	0.017286	0.012402
1999-12-28	0.013989	0.025512	0.014708	6.295335e-03	0.011801	3.174228e-03	-0.007295	-0.007831	-0.046676	-0.044463	-0.002195	-0.140307	-0.002417	-0.017053	-0.000538	0.012045	-0.041856	-0.079239	-0.003301	-0.009172	-0.009651	0.005969	0.009133	0.019109	0.038173	0.021448	0.021132	-0.058207	-0.005207	-0.033901	-0.030839	-0.013474	0.038353	-0.047250	0.027305	0.013849	0.011155	0.018918	-0.032547	-0.000544	0.002339	-0.010200	8.964579e-03	-0.032777	-0.034621	-0.010797	-0.041533	0.051845	0.017286	0.013629
1999-12-29	0.013688	0.024420	0.016528	8.160883e-03	0.008762	7.602279e-03	-0.008560	-0.010195	-0.013552	-0.056279	-0.007766	-0.142379	-0.006689	-0.016836	0.004718	0.020260	-0.042020	-0.059907	-0.002657	-0.008132	-0.008028	0.004279	0.009328	0.019012	0.036609	0.021275	0.011730	-0.045099	0.000341	-0.044399	-0.033929	0.018943	0.031140	-0.059410	0.019710	0.017879	0.030319	0.009254	-0.033269	-0.002303	0.000315	-0.005304	1.125478e-02	-0.040304	-0.043984	-0.001914	-0.032036	0.060213	-0.019145	0.027636

	Price	Simple_returns	Log_Returns	Cumulative_Log_Returns	Cumulative_simple_return
Date
2000-01-31	16264.63	0.014100	0.014002	0.014002	0.014100
2000-02-29	16193.06	-0.004400	-0.004410	0.009592	0.009638
2000-03-31	16058.66	-0.008300	-0.008334	0.001257	0.001258
2000-04-30	15800.11	-0.016100	-0.016231	-0.014974	-0.014862
2000-05-31	15926.51	0.008000	0.007968	-0.007006	-0.006981
2000-06-30	15775.21	-0.009500	-0.009545	-0.016551	-0.016415
2000-07-31	15585.91	-0.012000	-0.012072	-0.028624	-0.028218
2000-08-31	15984.91	0.025600	0.025278	-0.003346	-0.003340
2000-09-30	15746.73	-0.014900	-0.015012	-0.018358	-0.018191
2000-10-31	15885.31	0.008801	0.008762	-0.009596	-0.009550

	Total Return	Annualized Volatility	Sharpe Ratio	Max Drawdown	Sortino Ratio	Beta CTA	Beta SPY
SPY	2.401145	0.196481	0.358477	-0.564737	0.504624	0.030341	NaN
BarclayHedgeCTA	1.260104	0.058528	0.610281	-0.099127	1.068440	NaN	-0.034969
SMA EW	1.395916	0.084803	0.457838	-0.223292	0.689000	0.013432	-0.115380
SMA with Bands EW	1.373744	0.084273	0.455734	-0.225821	0.686411	0.014396	-0.114715
TSMOM EW	1.016600	0.082845	0.382761	-0.268425	0.563773	0.025145	-0.091794
RAMOM EW	0.454955	0.081090	0.226812	-0.348309	0.339012	0.004948	-0.068733
Kalman EW	-0.442506	0.072751	-0.287592	-0.469481	-0.394614	-0.000488	0.047530
SMA VW	1.057769	0.037591	0.793450	-0.100159	1.176060	0.005343	-0.054028
SMA with Bands VW	1.100254	0.037715	0.812835	-0.097420	1.193871	0.005799	-0.052074
TSMOM VW	1.019601	0.036822	0.788732	-0.125532	1.142775	0.006581	-0.047400
RAMOM VW	0.437113	0.035232	0.432955	-0.184973	0.633891	0.006416	-0.030480
Kalman VW	-0.291648	0.031524	-0.425595	-0.316244	-0.568984	-0.001630	0.025687

	Total Return	Annualized Volatility	Sharpe Ratio	Max Drawdown	Sortino Ratio	Beta CTA	Beta SPY
SPY	-0.079871	0.226033	-0.380768	-0.261071	-0.514466	-0.111787	NaN
BarclayHedgeCTA	0.004781	0.101166	0.108075	-0.048715	0.156686	NaN	-0.317347
SMA EW	0.099294	0.063000	1.953997	-0.045024	3.019277	0.039213	-0.147946
SMA with Bands EW	0.094613	0.062545	1.880423	-0.043751	2.886882	0.044260	-0.146286
TSMOM EW	0.107684	0.063726	2.085137	-0.028989	3.219221	0.070039	-0.144619
RAMOM EW	0.083373	0.064927	1.610360	-0.038224	2.333775	-0.027886	-0.119238
Kalman EW	-0.087078	0.056400	-2.037741	-0.105363	-2.604322	0.010887	0.027425
SMA VW	0.050666	0.031475	2.024549	-0.021106	3.251827	0.011611	-0.068056
SMA with Bands VW	0.042883	0.028715	1.885025	-0.017850	2.992080	0.013065	-0.057638
TSMOM VW	0.055765	0.031301	2.233580	-0.017844	3.634891	0.021920	-0.064178
RAMOM VW	0.057343	0.030722	2.337286	-0.014562	3.819972	0.006435	-0.046333
Kalman VW	-0.047006	0.027414	-2.232705	-0.062665	-2.789251	0.002779	0.013199

	Total Return	Annualized Volatility	Sharpe Ratio	Max Drawdown	Sortino Ratio	Beta CTA	Beta SPY
SPY	-0.381475	0.382405	-0.607081	-0.552144	-0.855154	0.003185	NaN
BarclayHedgeCTA	0.143656	0.068084	1.281422	-0.030139	3.019330	NaN	-0.055825
SMA EW	0.323348	0.168236	1.107603	-0.175243	1.723877	-0.061198	-0.235586
SMA with Bands EW	0.310761	0.167973	1.074089	-0.173921	1.666428	-0.060187	-0.234525
TSMOM EW	0.098651	0.160239	0.440829	-0.198377	0.649621	-0.036016	-0.235612
RAMOM EW	0.405536	0.156668	1.414032	-0.105063	2.193641	-0.053445	-0.156208
Kalman EW	-0.066683	0.115063	-0.311241	-0.137152	-0.438229	0.070349	0.054391
SMA VW	0.203827	0.087823	1.342277	-0.100159	2.117559	-0.020346	-0.119464
SMA with Bands VW	0.183774	0.083544	1.283044	-0.097420	1.993653	-0.026170	-0.114070
TSMOM VW	0.097428	0.084289	0.720027	-0.109756	1.081997	-0.019333	-0.125244
RAMOM VW	0.209940	0.081235	1.482650	-0.067923	2.321169	-0.015457	-0.078345
Kalman VW	-0.055474	0.062674	-0.528380	-0.103963	-0.711865	0.031790	0.032093

	Total Return	Annualized Volatility	Sharpe Ratio	Max Drawdown	Sortino Ratio	Beta CTA	Beta SPY
SPY	-0.097131	0.597390	-0.398187	-0.341047	-0.545211	2.467566	NaN
BarclayHedgeCTA	0.008523	0.045536	0.761660	0.000000	1.846975	NaN	-0.057977
SMA EW	0.053253	0.433450	0.678492	-0.147487	1.180354	-0.720125	-0.221315
SMA with Bands EW	0.054067	0.433776	0.685325	-0.146277	1.193522	-0.667976	-0.221680
TSMOM EW	0.010314	0.420297	0.296573	-0.147434	0.504697	-0.281847	-0.149386
RAMOM EW	0.101606	0.436384	1.083296	-0.137484	1.824874	-0.429474	-0.184418
Kalman EW	-0.170667	0.415100	-1.566797	-0.276783	-2.113758	0.644604	0.123475
SMA VW	0.019074	0.070510	1.089879	-0.030092	1.928763	-0.280779	-0.087152
SMA with Bands VW	0.019250	0.066508	1.161626	-0.025980	2.052963	-0.308394	-0.082187
TSMOM VW	0.007634	0.056040	0.561936	-0.026269	0.852489	-0.096721	-0.063279
RAMOM VW	0.049859	0.064282	3.013002	-0.020459	5.335903	-0.252097	-0.071098
Kalman VW	-0.096492	0.066415	-5.978148	-0.101195	-5.832602	0.195579	0.060143

	USD Future	EUR Future (USD)	JPY Future (USD)	GBP Future (USD)	GBP Future (EUR)	CAD Future (USD)	CHF Future (USD)	S&P 500	Nasdaq 100	S&P Canada 60	FTSE 100	DAX	SMI	CAC - 40	IBEX 35	Euro Stoxx 50	Nikkei 225	Hang Seng Index	US 2-year	US 5-year	US 10-year	Long Gilt (10 year)	Euro-Schatz-Futures (2 year)	Euro-Bobl-Future (5 year)	Euro-Bund-Future (10 year)	Euro-Buxl-Future (30 year)	Brent Crude Oil	WTI Crude Oil	Heating Oil	Natural Gas	Gold	Silver	Platinum	Palladium	Copper	Nickel	Zinc	Lean hogs	Live cattle	Feeder cattle	Wheat	Corn	Oat	Soybeans	Soybean oil	Frozen orange juice	Cocoa	Coffee	Sugar No.11	Cotton No.2
Date
2000-01-04	-0.013101	-0.018726	0.010262	-0.011188	-0.007459	0.002950	-0.019341	-0.026360	0.053766	-0.024980	0.024268	0.000525	0.015051	0.026219	0.025887	0.033067	-0.002820	-0.000763	-0.000966	-0.000927	-0.002986	0.001479	-0.002047	-0.003393	-0.000684	-0.013577	-0.027977	-0.001862	-0.001331	-0.067950	-0.020597	0.014430	0.020354	-0.016386	0.017536	0.015752	0.018352	-0.020434	0.007572	-0.004970	0.003022	0.029654	0.016105	0.026191	0.027786	-0.024958	-0.000955	0.075448	0.058878	0.000196
2000-01-05	-0.013597	-0.020362	-0.000830	-0.014146	-0.006201	0.004426	-0.021037	-0.025124	0.041037	-0.038664	0.007012	-0.010686	0.006016	-0.006442	0.003749	0.012921	0.036596	-0.083263	0.000370	0.002905	0.004382	-0.001378	-0.002047	-0.003490	-0.001744	-0.018382	-0.055324	-0.027244	-0.019638	-0.071633	-0.026239	-0.016802	-0.008567	-0.025011	0.027508	0.014910	0.013608	-0.010499	0.006121	-0.004386	0.013082	0.029654	0.013815	0.014592	0.018985	-0.001178	0.005273	0.095461	0.065739	0.016423
2000-01-06	-0.010701	-0.018433	-0.011737	-0.017488	-0.000962	0.011828	-0.019495	-0.031863	-0.017588	-0.052707	-0.004863	-0.020374	0.012579	-0.013708	0.003749	0.001362	-0.003358	-0.128352	0.001061	0.004648	0.007936	-0.001870	-0.001949	-0.003490	0.005241	-0.022851	-0.059636	-0.032569	-0.023716	-0.058801	-0.025193	-0.025100	-0.028436	-0.031424	0.031604	0.008266	0.015882	-0.008692	0.021944	0.003164	0.018100	0.025966	0.016105	0.017741	0.013344	0.012426	0.017215	0.110327	0.058878	0.006389
2000-01-07	-0.012288	-0.020080	-0.009859	-0.012044	0.002840	0.014795	-0.014391	0.007076	0.055030	-0.009351	-0.002856	0.029565	0.033208	0.003857	0.017414	0.034953	0.011613	-0.110091	0.000324	0.002444	0.004122	-0.002139	0.000097	0.001257	0.014646	-0.010918	-0.082458	-0.055469	-0.047077	-0.069330	-0.023414	-0.019663	-0.023361	-0.011863	0.032773	-0.001231	0.013608	-0.000564	0.010588	-0.002611	0.031093	0.010141	0.020680	0.010932	0.004668	0.013021	0.003070	0.086949	0.070954	-0.029073
2000-01-10	-0.013778	-0.022792	-0.008815	-0.013162	0.004581	0.014795	-0.017196	-0.003314	-0.001723	-0.030744	0.014605	0.014407	0.035470	-0.011311	0.015788	0.025289	0.011613	-0.069044	0.001430	0.004648	0.006572	0.004205	-0.000097	0.000484	0.017763	-0.011625	-0.055324	-0.037065	-0.047511	-0.049735	-0.022699	-0.020584	-0.017085	-0.029839	0.041595	-0.006629	0.003218	-0.001009	0.012002	-0.001747	0.024166	0.002921	0.020680	0.021957	0.017710	0.014808	0.019592	0.116300	0.072656	-0.021633

Masterarbeit FS24¶

Evaluating the Effectiveness of CTA Trend Following Strategies:¶

A Comparative Analysis against State-of-the-Art MA-Crossing Strategies¶

by Raphael Nussbaumer¶

1. Import Libraries¶

2. Data¶

Data Description¶

Making own DataFrames to delete the NA's¶

Check which Future is younger than 1999 and drop these¶

Correlations¶

Calculate the daily returns, annualized return and the annulized volatility¶

Create a Summary for the statistics¶

Data visualisation¶

3. Constructing the different Trend following Methods¶

1. Simple moving average crossover¶

Calculate the simple moving average¶

Creating the signal and indicate the trades¶

2. Simple moving average crossover with 1% band¶

Calculate the simple moving average¶

Creating the signal and indicate the trades¶

3. Time-series momentum¶

Calculate the-time series momentum¶

Creating the signal and indicate the trades¶

4. Risk-adujsted time series momentum¶

Calculate the risk-adjusted time series momentum¶

Creating the signal and indicate the trades¶

5. Kalman filter¶

Calculate the Kalman filter¶

Creating the signal and indicate the trades¶

4. Building portfolios and backtester¶

Initializing each Strategy¶

Define the start date and the initial value¶

1. SMA¶

Plot each MA strategy on its own¶

2. SMA with Bands¶

Plot each MA strategy on its own¶

3. TSMOM¶

Plot each MA strategy on its own¶

4. RAMOM¶

Plot each MA strategy on its own¶

5. Kalman Filter¶

Plot each MA strategy on its own¶

5. Building Portfolios¶

Equal weighted¶

1. SMA¶

2. SMA with Bands¶

3. TSMOM¶

4. RAMOM¶

5. Kalman filter¶

Volatility weighted¶

1. SMA¶

2. SMA with Bands¶

3. TSMOM¶

4. RAMOM¶

5. Kalman filter¶

6. Comparison¶

SPY¶

BarclyHedgeCTA Index¶

All equal weighted portfolios¶

Volatility weighted Portfolios vs BarclayHedge CTA Index¶

Subperiods¶

Early 2000s recession - 01.03.2001 - 30.11.2001¶

Great Recession - 01.12.2007 - 30.06.2009¶

Covid - 19 recession - 01.02.2020 - 30.04.2020¶